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APIs have become an integral part of modern digital infrastructure, and it can be easy to take their security for granted. But, unfortunately, APIs are a popular target for attackers. Hackers can use APIs to access crucial data and services, and breaching APIs allows attackers to bypass traditional security controls.
Most companies focus on speed of development and deployment ahead of security when crafting APIs, making them vulnerable to issues like insecure authentication, poor validation, or misconfigured endpoints, which attackers can abuse. Additionally, the interconnected nature of APIs creates multiple endpoints, widening the attack surface and creating additional points of entry that attackers can exploit.
As threats evolve and the attack surface grows to include more API endpoints, integrating AI threat detection and mitigation is an absolute must for businesses to take serious, deliberate action against API cyberattacks. Let’s find out why.
Staying ahead of zero-day API attacks
Of all the cyber attacks that commonly threaten APIs, zero-day attacks, leveraging unknown vulnerabilities, are probably the toughest to defeat. Traditional solutions rely more on the existence of preconfigured rules or signatures along with human interference to detect and block such attacks. This approach often fails against novel threats and can block legitimate traffic, leaving applications vulnerable and making APIs inaccessible to users.
APIs must balance between allowing legitimate users access and maintaining security. AI and ML technologies excel at identifying zero-day attacks based on pattern and behavior analysis rather than known signatures. For instance, heuristic algorithms can detect anomalies, such as sudden spikes in unusual traffic or behaviors indicative of malicious intent.
Consider the following example: A certain IP address makes an abnormally large number of requests to a rarely accessed endpoint. Even without prior knowledge of the IP or attack vector, an AI/ML-enhanced solution can flag the activity as suspicious and block it proactively. Using minimal indicators, such as frequency patterns or traffic anomalies, AI can stop attackers before they fully exploit vulnerabilities. Additionally, this means that only suspicious IPs are blocked, and legitimate users can continue to access APIs unimpeded.
The risks of shadow APIs
One of the biggest risks is shadow APIs, which are endpoints that exist but aren't documented or monitored. These can arise from configuration mistakes, forgotten updates, or even rogue development practices. These unknown APIs are the ideal target for Layer 7 attacks, as they are often left undefended, making them easy targets.
AI-powered API discovery tools map both known and unknown API endpoints, enabling the grouping and management of these endpoints so sensitive APIs can be properly secured. This level of visibility is critical to securing systems against API-targeting attacks; without it, businesses are left in the dark.
API discovery as a critical security practice
WAAP with AI/ML capabilities excels in API security because it accurately checks and analyzes API traffic. The Gcore API discovery engine offers 97 to 99 percent accuracy, mapping APIs in users’ domains and using data to recommend policies to help secure APIs.
How heuristics enhance WAAP AI capabilities to protect APIs
While AI and ML form the backbone of modern WAAPs, heuristic methods complement them in enhancing detection accuracy. Heuristics allow the system to inspect granular behaviors, such as mouse clicks or scrolling patterns, which distinguish legitimate users from bots.
For example, most scraping attacks involve automated scripts that interact with APIs in predictable and repetitive manners. In those cases, WAAP can use request patterns or user action monitoring to identify the script with high accuracy. Heuristics may define bots by checking how users interact with page elements, such as buttons or forms, and flagging those that behave unnaturally.
This layered approach ensures that the most sophisticated automated attack attempts are caught in the net and mitigated without affecting legitimate traffic.
Protect your APIs with the click of a button using Gcore WAAP
AI offers proactive, intelligent solutions that can address the modern complexities of cybersecurity. These technologies empower organizations to secure APIs against even the most sophisticated threats, including zero-day vulnerabilities and undiscovered APIs.
Interested in protecting your APIs with WAAP? Download our ebook to discover cybersecurity best practices, the most prevalent threats, and how WAAP can protect your business’s digital infrastructure, including APIs. Or, reach out to our team to learn more about Gcore WAAP.
Related articles
What is API Rate Limiting?
API rate limiting is the process of controlling how many requests a user or system can make to an API within a specific timeframe. This mechanism caps transactions to prevent server overload and ensures fair distribution of resources across all users.Rate limiting serves as both a security measure and a quality control tool for modern APIs. It protects systems from abuse by restricting excessive requests from a single source. This helps prevent brute force attacks, credential stuffing, and denial-of-service attempts.The GitHub REST API, for example, allows a maximum of 5,000 requests per hour per authenticated user to maintain system stability.The necessity of rate limiting becomes clear when you consider resource allocation and cost management. Without limits, a single user can monopolize API resources, degrading performance for everyone else. This is especially important for multi-tenant platforms and tiered subscription models, where fair usage directly impacts service quality and revenue.Rate limiting works through different algorithms that track and restrict request volumes.These systems monitor incoming requests and apply rules based on factors like IP address, API key, or user account. Typical limits range from tens to thousands of requests per second, depending on the provider and service tier. The Zoom API, for instance, varies its rate limits by endpoint but typically restricts requests per minute per account.Understanding rate limiting is important because it affects how you design, build, and maintain API integrations.Proper implementation protects your infrastructure from overload while ensuring consistent performance for legitimate users. For technical teams, this means more reliable systems and better resource planning.What is API rate limiting?API rate limiting controls the number of requests a user or application can make to an API within a specific timeframe. For example, you might set limits at 5,000 requests per hour or 100 requests per minute.This mechanism protects your APIs from overload and abuse. It ensures fair resource distribution across all users while maintaining consistent performance for everyone accessing the API. Rate limiting works as both a security measure (defending against credential stuffing and denial-of-service attacks) and a quality control tool that keeps your services running smoothly.Why is API rate limiting necessary?API rate limiting prevents server overload and ensures fair distribution of resources across all users. Without rate limits, a single client could flood an API with requests, consuming excessive bandwidth and processing power that degrades performance for everyone else. This protection is crucial for multi-tenant systems, where multiple users share the same infrastructure.Rate limiting also defends against malicious attacks. It blocks brute force attempts, credential stuffing, and denial-of-service attacks by limiting the number of requests that can originate from a single source. For example, GitHub's REST API caps authenticated users at 5,000 requests per hour to prevent abuse while maintaining service stability.Rate limiting ensures consistent API performance as demand grows. By capping transactions per second or data volume per user, providers maintain steady response times even during traffic spikes.This controlled access protects backend systems from unexpected load while keeping the API responsive for legitimate users across different subscription tiers.How does API rate limiting work?API rate limiting works by monitoring and controlling the number of requests a client can make to an API within a defined time window. The system tracks each incoming request, identifies the client through their API key, IP address, or authentication token, and checks whether they've exceeded their allowed quota. If they're within limits, the request processes normally.If they've hit their cap, the API returns an error response (typically HTTP 429 "Too Many Requests") and blocks the request until the time window resets.Here's how the process works: When a request arrives at the API gateway, the system logs the client identifier and timestamps the request. It then compares this against stored rate limit rules, which specify thresholds like 100 requests per minute or 5,000 requests per hour.Different algorithms handle this counting differently. Token bucket algorithms refill available requests at steady rates. Fixed window counters reset at specific intervals. Sliding window logs track exact request timestamps for precise control.Rate limiting protects APIs from overload by preventing any single client from consuming too many resources. It blocks brute force attacks, where attackers try thousands of password combinations rapidly. It stops denial-of-service attempts that flood servers with requests. It ensures fair access across all users, which is especially critical for multi-tenant platforms where one client's excessive usage could degrade performance for everyone else.What are the main rate limiting algorithms?Rate limiting algorithms refer to the technical methods used to control and measure API request rates within defined time windows. Here are the main rate limiting algorithms.Token bucket: This algorithm adds tokens to a bucket at a fixed rate. Each request consumes one token. When the bucket is empty, requests are rejected until new tokens are added. It allows brief bursts of traffic while maintaining average rate limits.Leaky bucket: Requests enter a queue and are processed at a constant rate, like water dripping from a bucket with a small hole. This algorithm smooths out traffic spikes by enforcing a steady outflow rate. It's ideal when you need predictable, uniform request processing.Fixed window: This method counts requests within fixed time intervals, such as allowing 1,000 requests per hour starting at each hour mark. The counter resets at the start of each new window. It's simple to implement but can allow twice the limit if users time requests at window boundaries.Sliding window log: The system maintains a timestamped log of each request. It counts only those within the current time window. This approach provides precise rate limiting without boundary issues. It requires more memory to store individual request timestamps.Sliding window counter: This hybrid combines fixed window counters with weighted calculations from the previous window. It approximates sliding window log accuracy with less memory overhead. The algorithm weighs the previous window's count based on time overlap with the current window.Concurrent requests: This algorithm limits the number of simultaneous active requests rather than counting total requests over time. It's useful for protecting resources that can only handle a specific number of parallel operations. The limit decreases when requests complete and increases when new ones start.What are the different rate limiting methods?Rate limiting methods refer to the specific algorithms and techniques used to control and restrict the number of API requests within defined time frames. Here are the different rate limiting methods.Fixed window: This method divides time into fixed intervals (like one-minute blocks) and allows a set number of requests per interval. It's simple to implement, however, each window resets at a predetermined time, making it vulnerable to traffic spikes at window boundaries.Sliding window log: The system maintains a timestamped log of each request and counts only those within the rolling time window. This approach provides precise rate limiting but requires more memory to store request timestamps for each user.Sliding window counter: This hybrid method combines the simplicity of a fixed window with the accuracy of a sliding window. It weighs requests from the current and previous windows, balancing memory efficiency with smoother rate enforcement across window transitions.Token bucket: Users receive a bucket filled with tokens that replenish at a steady rate. Each request consumes one token. This method allows brief traffic bursts while maintaining long-term rate control, making it ideal for APIs with variable traffic patterns.Leaky bucket: Requests enter a queue that processes them at a constant rate, like water dripping from a bucket with a hole. The queue smooths out traffic spikes and enforces steady request processing. However, it can delay legitimate requests during high traffic.Concurrent rate limiting: This method restricts the number of simultaneous active requests, rather than the total number of requests over time. It prevents resource exhaustion from long-running requests and works well for APIs with expensive operations.What are the benefits of implementing API rate limiting?API rate limiting gives organizations control over how many requests users can make to their APIs within specific timeframes. Here are the key benefits.Server protection: Rate limiting prevents system overload by capping requests at manageable levels. Your servers stay responsive even during traffic spikes or unexpected demand surges.Fair resource distribution: Rate limits ensure no single user monopolizes API capacity. This matters especially for multi-tenant platforms where all clients need consistent access regardless of other users' activity.Security against attacks: Rate limiting blocks brute force attempts, credential stuffing, and denial-of-service attacks by restricting excessive requests from single sources. This defense layer stops malicious actors before they can damage your infrastructure.Cost control: Capping API requests prevents unexpected infrastructure costs from runaway usage or automated scripts. You can predict and manage server capacity needs more accurately.Performance consistency: Rate limits maintain steady response times across all users as demand grows. Your systems handle gradual scaling without degrading service quality for existing clients.API monetization support: Rate limiting enables tiered pricing models, where different subscription levels receive varying request allowances. This creates clear value distinctions between free, standard, and premium API access tiers.Resource planning: Historical rate limit data shows actual usage patterns. You can make informed decisions about infrastructure investments, identify which endpoints require more capacity, and pinpoint underutilized resources.What are common challenges of API rate limiting?Common challenges with API rate limiting refer to the obstacles and difficulties developers and organizations face when implementing, configuring, and managing request limits on their APIs. The common challenges with API rate limiting are listed below.Choosing appropriate limits: Setting rate limits too low can frustrate legitimate users while blocking valid traffic. Set them too high and you won't protect against abuse. Finding the right balance requires analyzing usage patterns and understanding peak demand periods.Distributed system complexity: Rate limiting across multiple servers and regions creates synchronization problems. Each node must accurately track request counts, but this distributed state management can lead to inconsistent enforcement. Users may exceed limits before all servers update their counts.User experience degradation: When users hit rate limits, they receive error responses that disrupt their workflow and create frustration. Poor error messages or lack of retry guidance makes things worse, leaving users uncertain about when they can resume requests.Identifying legitimate users: Distinguishing between malicious actors and legitimate high-volume users is difficult, especially when multiple users share IP addresses behind corporate firewalls or NAT gateways. This challenge increases when APIs serve both human users and automated systems with different usage patterns.Managing multiple limit tiers: APIs with different subscription levels must enforce varied rate limits for free, basic, and premium users. This tiered approach requires complex tracking logic and clear communication about each tier's restrictions.Handling burst traffic: Legitimate use cases often require short bursts of requests that exceed average limits, such as batch processing or data synchronization. Strict rate limiting blocks these valid patterns. Developers must then implement complex retry logic or request queuing.Monitoring and alerting: Tracking rate limit violations across thousands of users generates massive amounts of data that's difficult to analyze. Identifying patterns that indicate attacks versus normal usage spikes requires sophisticated monitoring tools and clear metrics.How to implement API rate limitingYou implement API rate limiting by choosing a rate limiting algorithm, defining request thresholds, and enforcing those limits at the API gateway or application level.First, select a rate limiting algorithm that matches your needs. Token bucket works well for handling burst traffic while maintaining average rates. Fixed window counting is more straightforward but less precise. Sliding window log provides the most accuracy but requires more memory to track individual request timestamps.Next, define your rate limit thresholds based on user tiers and API capacity. Set specific limits, such as 100 requests per minute for free users and 5,000 requests per hour for premium accounts. Test these limits under load to confirm your infrastructure can handle the maximum allowed request rate.Then, choose your rate limiting identifier to track requests. You can limit by API key for authenticated users, by IP address for public endpoints, or by user account for multi-tenant applications. API keys provide the most control, preventing users from bypassing limits by switching IP addresses.After that, add rate limit headers to your API responses so clients know their current status. Include X-RateLimit-Limit for the maximum requests allowed, X-RateLimit-Remaining for requests left in the current window, and X-RateLimit-Reset for when the limit resets.Set up proper error responses when clients exceed their limits. Return HTTP 429 (Too Many Requests) status code with a clear message explaining the limit and reset time. Include a Retry-After header to tell clients when they can make requests again.Finally, add monitoring and alerting for rate limit violations. Track which clients hit limits most frequently and adjust thresholds if legitimate users face restrictions. Monitor for patterns that suggest abuse attempts like rapid-fire requests from single sources. Start with conservative limits and adjust based on real usage patterns. Don't set overly restrictive thresholds that frustrate legitimate users.What are API rate limiting best practices?API rate limiting best practices are the proven methods and strategies organizations use to implement effective rate controls on their APIs. Here are the key best practices.Define clear limits: Set specific request thresholds based on your API's capacity and user tiers. Start with conservative limits, such as 100 requests per minute for free users and 1,000 for paid accounts. Then adjust based on actual usage patterns.Choose the right algorithm: Select a rate limiting algorithm that matches your needs. Token bucket works well for burst traffic, leaky bucket for steady flow, or sliding window for precise control. Each algorithm has different trade-offs between accuracy and resource consumption.Apply multiple limit layers: Implement rate limits at different levels, including per user, per API key, per IP address, and per endpoint. This multi-layer approach prevents abuse while maintaining flexibility for legitimate high-volume users.Return clear error responses: Send HTTP 429 status codes with detailed headers showing the limit, remaining requests, and reset time. Include retry-after information so clients know exactly when they can make requests again.Monitor and alert: Track rate limit hits, rejected requests, and usage patterns across all endpoints. Set up alerts when users consistently hit limits. This may indicate a legitimate need for higher tiers or potential abuse attempts.Document limits publicly: Publish your rate limits, algorithms, and policies in API documentation so developers can design their applications accordingly. Include examples of how to handle rate limit responses and implement exponential backoff.Implement gradual enforcement: Start with logging and warnings before implementing rigid enforcement, allowing users time to adjust. This approach reduces friction and helps identify issues with your limit settings before they impact production applications.Use distributed rate limiting: Store rate limit counters in distributed caches like Redis when running multiple API servers. This ensures accurate counting across your infrastructure and prevents users from bypassing limits by hitting different servers.Frequently asked questionsWhat happens when an API rate limit is exceeded?When an API rate limit is exceeded, the server returns an HTTP 429 "Too Many Requests" error. It then blocks further requests until the rate limit window resets.The response typically includes headers that show when you can retry, such as Retry-After or X-RateLimit-Reset. This lets clients pause and resume requests automatically.How do I choose the correct rate limit for my API?Start with conservative limits based on your expected traffic patterns. For standard users, 100 requests per minute is a solid baseline. You can adjust upward as you collect monitoring data and gain a better understanding of your actual usage.When setting your limits, consider these key factors: average request size, peak usage times, and database query costs. You'll also want to consider whether read and write operations require different thresholds. Read operations typically handle higher volumes, while writes often need tighter controls to protect your infrastructure.Match your rate limits to your user tiers and infrastructure capacity. Monitor performance closely during the first few weeks, then fine-tune your limits based on real-world data.What's the difference between rate limiting and API throttling?Rate limiting and API throttling are the same thing. Both control the number of API requests you can make within a specified timeframe. This prevents system overload and ensures fair resource distribution across all users.Can rate limiting affect legitimate users?Yes, rate limiting can temporarily block legitimate users if they exceed request thresholds. This typically occurs during traffic spikes or when users share IP addresses with high-volume requesters. You can reduce this impact by setting reasonable thresholds and providing clear error messages that help real users understand what's happening.How do I communicate rate limits to API consumers?Use HTTP response headers to communicate rate limits clearly. Return three key headers with each API response: X-RateLimit-Limit (total allowed requests), X-RateLimit-Remaining (requests left), and X-RateLimit-Reset (time until the limit resets). When consumers exceed their limits, return a 429 status code with a Retry-After header that shows exactly when they can resume requests.What HTTP status code is used for rate limiting?HTTP status code 429 (Too Many Requests) is the standard code for rate limiting. When a client exceeds the allowed number of requests, the server returns this code to signal they need to wait before trying again. The response typically includes a Retry-After header that indicates to the client when they can retry.Should rate limits differ for authenticated vs. unauthenticated requests?Yes, authenticated requests should have higher rate limits than unauthenticated ones. Here's why: authenticated users are identifiable and traceable, which means you can monitor their behavior and hold them accountable. They typically have legitimate use cases that justify more API access, making them lower-risk than anonymous users.
What is Bot mitigation?
Bot mitigation is the process of detecting, managing, and blocking malicious bots or botnet activity from accessing websites, servers, or IT ecosystems to protect digital assets and maintain a legitimate user experience. Malicious bots accounted for approximately 37% of all internet traffic in 2024, up from 32% in 2023.Understanding why bot mitigation matters starts with the scope of the threat. Automated traffic surpassed human activity for the first time in 2024, reaching 51% of all web traffic according to Research Nester.This shift is significant. More than half of your web traffic isn't human, and a large portion of that automated traffic is malicious.The types of malicious bots vary in complexity and threat level. Simple bad bots perform basic automated tasks, while advanced persistent bots use complex evasion techniques. AI-powered bots represent the most advanced threat. They mimic human behavior to bypass defenses and can adapt to detection methods in real time.Bot mitigation systems work by analyzing traffic patterns, behavior signals, and request characteristics to distinguish between legitimate users and automated threats.These systems identify bad bots engaging in credential stuffing, scraping, fraud, and denial-of-service attacks. The technology combines signature-based detection, behavioral analysis, and machine learning models to stop threats before they cause revenue loss or reputational damage.The bot mitigation market reflects the growing importance of this technology, valued at over $654.93 million in 2024 and projected to exceed $778.58 million in 2025. With a compound annual growth rate of more than 23.6%, the market will reach over $10.29 billion by 2037.What is bot mitigation?Bot mitigation detects, manages, and blocks malicious automated traffic from accessing websites, applications, and servers while allowing legitimate bots to function normally. This security practice protects your digital assets from threats like credential stuffing, web scraping, fraud, and denial-of-service attacks that cause revenue loss and damage user experience.Modern solutions use AI and machine learning to analyze behavioral patterns. They distinguish between harmful bots, helpful bots like search engine crawlers, and real human users.Why is bot mitigation important?Bot mitigation is important because malicious bots now make up 37% of all internet traffic, threatening business operations through credential stuffing, web scraping, fraud, and denial-of-service attacks that cause revenue loss and damage brand reputation.The threat continues to grow rapidly. Automated traffic surpassed human activity for the first time in 2024, reaching 51% of all web traffic. This shift reflects how AI and machine learning enable attackers to create bots at scale that mimic human behavior and evade traditional security defenses.Without effective mitigation, businesses face direct financial impact. E-commerce sites lose revenue to inventory hoarding bots and price scraping. Financial services suffer from account takeover attempts. Media companies see ad fraud drain marketing budgets.Modern bots don't just follow simple scripts. Advanced persistent bots rotate IP addresses, solve CAPTCHAs, and adjust behavior patterns to blend with legitimate users. This arms race drives organizations to adopt AI-powered detection that analyzes behavioral patterns rather than relying on static rules that bots quickly learn to bypass.What are the different types of malicious bots?Scraper bots: Extract content, pricing data, and proprietary information from websites without permission, stealing intellectual property and reducing content value.Credential stuffing bots: Test stolen username and password combinations to gain unauthorized access and enable fraud.DDoS bots: Flood servers with traffic to cause outages, operating within large botnets.Inventory hoarding bots: Purchase or reserve limited items faster than humans, causing revenue loss and customer frustration.Spam bots: Post fake reviews, malicious links, and phishing content across platforms.Click fraud bots: Generate fake ad clicks to waste competitors' budgets or inflate metrics.Account creation bots: Generate fake accounts at scale for scams and fraud schemes.Vulnerability scanner bots: Probe systems for weaknesses and unpatched software for exploitation.How does bot mitigation work?Bot mitigation systems analyze and block harmful automated traffic before it impacts your site or infrastructure. They use behavioral analysis, machine learning, and layered defenses to distinguish legitimate users from malicious bots.Modern solutions track user interactions such as mouse movement, keystroke rhythm, and browsing speed to detect automation. Suspicious requests undergo CAPTCHA or JavaScript challenges. IP reputation databases and rate-limiting rules stop repetitive requests and brute-force attacks.If a request fails behavioral or reputation checks, it’s blocked at the edge—preventing resource strain and service disruption.What are the key features of bot mitigation solutions?Real-time detection: Monitors and blocks threats as they occur to protect resources instantly.Behavioral analysis: Tracks how users interact with a site to spot non-human patterns.Machine learning models: Continuously adapt to detect new bot types without manual rule updates.CAPTCHA challenges: Confirm human presence when suspicious behavior is detected.Rate limiting: Restricts excessive requests to prevent automated abuse.Device fingerprinting: Identifies repeat offenders even if IPs change.API protection: Secures programmatic access points from automated abuse.How to detect bot traffic in your analyticsCheck for high bounce rates and short session durations; bots often leave quickly.Look for traffic spikes from unusual regions or suspicious referrals.Inspect user-agent strings for outdated or missing browser identifiers.Analyze navigation paths; bots access pages in unnatural, rapid sequences.Monitor form submissions for identical inputs or unrealistic completion speeds.Track infrastructure performance; sudden server load spikes may indicate bot activity.What are the best bot mitigation techniques?Behavioral analysis: Use ML to detect non-human interaction patterns.CAPTCHA challenges: Add human-verification steps for risky requests.Rate limiting: Restrict excessive requests from the same source.Device fingerprinting: Track hardware and browser identifiers to catch rotating IPs.Challenge-response tests: Use JavaScript or proof-of-work tasks to filter out bots.IP reputation scoring: Block or challenge traffic from suspicious IP ranges.Machine learning detection: Continuously train detection models on evolving bot behavior.How to choose the right bot mitigation solutionIdentify your threat profile—scraping, credential stuffing, or DDoS attacks.Evaluate detection accuracy, focusing on behavioral and ML capabilities.Test the system’s impact on user experience and latency.Ensure integration with existing WAF, CDN, and SIEM tools.Compare pricing by traffic volume and overage handling.Choose AI-powered systems that adapt automatically to new threats.Review dashboards and reports for visibility into bot activity and ROI.Frequently asked questionsWhat's the difference between bot mitigation and bot management?Bot mitigation focuses on blocking malicious bots, while bot management identifies and controls all bot traffic—allowing helpful bots while blocking harmful ones.How much does bot mitigation cost?Costs range from $200 to $2,000 per month for small to mid-sized businesses, scaling to over $50,000 annually for enterprise setups. Pricing depends on traffic volume and feature complexity.Can bot mitigation solutions block good bots like search engines?No. Modern systems use allowlists and behavioral analysis to distinguish legitimate crawlers from malicious automation.How long does it take to implement bot mitigation?Typical deployment takes one to four weeks, depending on your infrastructure complexity and deployment model.What industries benefit most from bot mitigation?E-commerce, finance, gaming, travel, and media services benefit most—these sectors face the highest risks of scraping, credential stuffing, and fraudulent automation.How do I know if my website needs bot mitigation?If you notice traffic anomalies, scraping, credential attacks, or degraded performance, your site likely needs protection.Does bot mitigation affect website performance?Minimal latency—typically 1–5 ms—is added. Edge-based detection ensures real users experience fast load times while threats are filtered in real time.Protect your platform with Gcore SecurityGcore Security offers advanced bot mitigation as part of its Web Application Firewall and edge protection suite. It detects and blocks malicious automation in real time using AI-powered behavioral analysis, ensuring legitimate users can always access your services securely.With a globally distributed network and low-latency edge filtering, Gcore Security protects against scraping, credential stuffing, and DDoS attacks—without slowing down your applications.
Good bots vs Bad Bots
Good bots vs bad bots is the distinction between automated software that helps websites and users versus programs designed to cause harm or exploit systems. Malicious bot attacks cost businesses an average of 3.6% of annual revenue.A bot is a software application that runs automated tasks on the internet. It handles everything from simple repetitive actions to complex functions like data scraping or form filling. These programs work continuously without human intervention, performing their programmed tasks at speeds no person can match.Good bots perform helpful tasks for companies and website visitors while following ethical guidelines and respecting website rules such as robots.txt files. Search engine crawlers like Googlebot and Bingbot index web content. Social network bots, like Facebook crawlers, gather link previews. Monitoring bots check site uptime and performance.Bad bots work with malicious intent to exploit systems, steal data, commit fraud, disrupt services, or gain competitive advantage without permission. They often ignore robots.txt rules and mimic human behavior to evade detection, making them harder to identify and block. The OWASP Automated Threat Handbook lists 21 distinct types of bot attacks that organizations face.Understanding the difference between good and bad bots is critical for protecting your business. Companies with $7 billion or more in revenue face estimated annual damages of $250 million or more from bad bot activity. This makes proper bot management both a technical and financial priority.What is a bot?A bot is a software application that runs automated tasks on the internet. It performs actions ranging from simple repetitive operations to complex functions like data scraping, form filling, and content indexing.Bots work continuously without human intervention. They execute programmed instructions at speeds far beyond human capability. They're classified mainly as good or bad based on their intent and behavior. Good bots follow website rules and provide value. Bad bots ignore guidelines and cause harm through data theft, fraud, or service disruption.What are good bots?Good bots are automated software programs that perform helpful online tasks while following ethical guidelines and respecting website rules. Here are the main types of good bots:Search engine crawlers: These bots index web pages to make content discoverable through search engines like Google and Bing. They follow robots.txt rules and help users find relevant information online.Site monitoring bots: These programs check website uptime and performance by regularly testing server responses and page load times. They alert administrators to downtime or technical issues before users experience problems.Social media crawlers: Platforms like Facebook and LinkedIn use these bots to fetch content previews when users share links. They display accurate titles, descriptions, and images to improve the sharing experience.SEO and marketing bots: Tools like SEMrush and Ahrefs use bots to analyze website performance, track rankings, and audit technical issues. They help businesses improve their online visibility and fix technical problems.Aggregator bots: Services like Feedly and RSS readers use these bots to collect and organize content from multiple sources. They deliver fresh content to users without requiring manual checks of each website.Voice assistant crawlers: Digital assistants like Alexa and Siri use bots to gather information for voice search responses. They index content specifically formatted for spoken queries and conversational interactions.Copyright protection bots: These programs scan the web to identify unauthorized use of copyrighted content like images, videos, and text. They help content creators protect their intellectual property and enforce usage rights.What are bad bots?Bad bots are automated software programs designed with malicious intent to exploit systems, steal data, commit fraud, disrupt services, or gain competitive advantage without permission. Here are the most common types you'll encounter:Credential stuffing bots: These bots automate login attempts using stolen username and password combinations to breach user accounts. They target e-commerce sites and login pages, testing thousands of credentials per minute until they find valid account access.Web scraping bots: These programs extract content, pricing data, or proprietary information from websites without permission. Competitors often use them to steal product catalogs, pricing strategies, or customer reviews for their own advantage.DDoS attack bots: These bots flood servers with excessive traffic to overwhelm systems and cause service outages. A coordinated botnet can generate millions of requests per second, making websites unavailable to legitimate users.Inventory hoarding bots: These bots automatically purchase limited inventory items like concert tickets or sneakers faster than human users can complete transactions. Scalpers then resell these items at inflated prices, causing revenue loss and customer frustration.Click fraud bots: These programs generate fake clicks on pay-per-click advertisements to drain competitors' advertising budgets. They can also artificially inflate website traffic metrics to create misleading analytics data.Spam bots: These automated programs post unwanted comments, create fake accounts, or send mass messages across websites and social platforms. They spread malicious links, phishing attempts, or promotional content that violates platform rules.Vulnerability scanning bots: These bots probe websites and networks to identify security weaknesses that attackers can exploit. They ignore robots.txt rules and mimic human behavior patterns to avoid detection while mapping system vulnerabilities.What are the main differences between good bots and bad bots?The main differences between good bots and bad bots refer to their intent, behavior, and impact on websites and online systems. Here's what sets them apart:Intent and purpose: Good bots handle helpful tasks like indexing web pages for search engines, monitoring site uptime, or providing customer support through chatbots. Bad bots are built with malicious intent. They exploit systems, steal data, commit fraud, or disrupt services.Rule compliance: Good bots follow website rules and respect robots.txt files, which tell them which pages they can or can't access. Bad bots ignore these rules. They often try to access restricted areas of websites to extract sensitive information or find vulnerabilities.Behavior patterns: Good bots work transparently with identifiable user agents and predictable access patterns that make them easy to recognize. Bad bots mimic human behavior and use evasion techniques to avoid detection, making them harder to identify and block.Value creation: Good bots provide value to website owners and visitors by improving search visibility, enabling content aggregation, and supporting essential internet functions. Bad bots cause harm through credential stuffing attacks, data scraping, account takeovers, and DDoS attacks that overload servers.Economic impact: Good bots help businesses drive organic traffic, monitor performance, and improve customer service efficiency. Bad bots cost businesses money. Companies experience an average annual revenue loss of 3.6% due to malicious bot attacks.Target selection: Good bots crawl websites systematically to gather publicly available information for legitimate purposes like search indexing or price comparison. Bad bots specifically target e-commerce sites, login pages, and payment systems to breach accounts, steal personal data, and commit fraud.What are the types of bad bot attacks?The types of bad bot attacks listed below refer to the different methods malicious bots use to exploit systems, steal data, commit fraud, or disrupt services:Credential stuffing: Bots automate login attempts using stolen username and password combinations from previous data breaches. They target e-commerce sites, banking platforms, and any service with user accounts.Web scraping: Bots extract large amounts of content, pricing data, or product information from websites without permission. Competitors often use this attack to copy content or undercut prices.DDoS attacks: Bots flood servers with massive traffic to overwhelm systems and crash websites, causing downtime and revenue loss.Account takeover: Bots breach user accounts by testing stolen credentials or exploiting weak passwords. Once inside, they make fraudulent purchases or steal personal information.Inventory hoarding: Bots add products to shopping carts faster than humans can, preventing legitimate purchases. Scalpers use them to resell limited items at inflated prices.Payment fraud: Bots test stolen credit card numbers by making small transactions to identify active cards. Merchants face chargebacks and account suspensions as a result.Click fraud: Bots generate fake ad clicks to drain competitors' budgets or inflate publisher revenue, costing the digital advertising industry billions annually.Gift card cracking: Bots systematically test gift card number combinations to find active cards and drain their balances. This attack mimics legitimate behavior, making detection difficult.How can you detect bot traffic?You detect bot traffic by analyzing patterns in visitor behavior, request characteristics, and technical signatures that automated programs leave behind. Most detection methods combine multiple signals to identify bots accurately, since sophisticated bots try to mimic human behavior.Start by examining traffic patterns. Bots often access pages at inhuman speeds, click through dozens of pages per second, or submit forms instantly. They also visit at unusual times or generate sudden spikes from similar IP addresses.Check technical signatures in HTTP requests. Bots frequently use outdated or suspicious user agents, lack JavaScript execution, or disable cookies. They might also have missing headers that browsers usually send. Good bots identify themselves clearly; bad bots forge or rotate identifiers.Monitor interaction patterns. Bots typically fail CAPTCHA challenges, show repetitive clicks, and follow linear navigation paths unlike real users. Behavioral analysis tools track mouse movements, scrolling, and typing speed to flag automation.Modern detection systems use machine learning to analyze hundreds of signals, such as session duration, scroll depth, or keystroke dynamics, to distinguish legitimate from automated traffic with high accuracy.How to protect your website from bad botsYou protect your website from bad bots by implementing a layered defense strategy that combines traffic monitoring, behavior analysis, and access controls.Deploy a web application firewall (WAF) that identifies and blocks known bot signatures based on IP, user agent, and behavior patterns.Implement CAPTCHA challenges on login, checkout, and registration pages to distinguish humans from bots.Analyze server logs for abnormal traffic patterns such as repeated requests or activity spikes from similar IP ranges.Set up rate limiting rules to restrict how many requests a single IP can make per minute. Adjust thresholds based on your normal user behavior.Monitor and enforce robots.txt to guide good bots and identify those that ignore these rules.Use bot management software that analyzes behavior signals like mouse movement or navigation flow to detect evasion.Maintain updated blocklists and subscribe to threat intelligence feeds that report new malicious bot networks.What are the best bot management solutions?The best bot management solutions are software platforms and services that detect, analyze, and mitigate automated bot traffic to protect websites and applications from malicious activity. The best bot management solutions are listed below:Behavioral analysis tools: Track mouse movements, keystrokes, and navigation to distinguish humans from bots. Advanced systems detect even those that mimic human activity.CAPTCHA systems: Challenge-response tests that verify human users, including invisible CAPTCHAs that analyze behavior without user input.Rate limiting controls: Restrict request frequency per IP or session to stop brute-force and scraping attacks.Device fingerprinting: Identify unique devices across sessions using browser and system attributes, even with rotating IPs.Machine learning detection: Use adaptive models that learn new attack patterns and evolve automatically to improve accuracy.Web application firewalls: Filter and block malicious HTTP traffic, protecting against both bot-based and application-layer attacks.Frequently asked questionsHow can you tell if a bot is good or bad?You can tell if a bot is good or bad by checking its intent and behavior. Good bots follow website rules like robots.txt, provide value through tasks like search indexing or customer support, and identify themselves clearly. Bad bots ignore these rules, mimic human behavior to evade detection, and work with malicious intent to steal data, commit fraud, or disrupt services.Do good bots ever cause problems for websites?Yes, good bots can cause problems when they crawl too aggressively. They consume excessive bandwidth and server resources, slowing performance for real users. Rate limiting and robots.txt configurations help manage legitimate bot traffic.What happens if you block good bots accidentally?Blocking legitimate bots can harm your SEO, break integrations, or stop monitoring services. Check your logs, identify the bot, and whitelist verified IPs or user agents before restoring access.Can bad bots bypass CAPTCHA verification?Yes, advanced bad bots can bypass CAPTCHA verification using solving services, machine learning, or human-assisted methods. Some services solve 1,000 CAPTCHAs for as little as $1.How much internet traffic is from bad bots?Bad bot traffic accounts for approximately 30% of all internet traffic, meaning nearly one in three web requests comes from malicious automated programs.What is the difference between bot management and WAF?Bot management detects and controls automated traffic, both good and bad. A WAF filters malicious HTTP/HTTPS requests to block web application attacks like SQL injection and XSS. Together, they provide layered protection.Are all web scrapers considered bad bots?No, not all web scrapers are bad bots. Search engine crawlers and monitoring tools work ethically and provide value. Scrapers become bad bots when they ignore rules, steal data, or overload servers to gain unfair advantage.
What is DNS Cache Poisoning?
DNS cache poisoning is a cyberattack in which false DNS data is inserted into a DNS resolver's cache, causing users to be redirected to malicious sites instead of legitimate ones. As of early 2025, over 30% of DNS resolvers worldwide remain vulnerable to these attacks.DNS works by translating human-readable domain names into IP addresses that computers can understand. DNS resolvers cache these translations to improve performance and reduce query time.When a cache is poisoned, the resolver returns incorrect IP addresses. This sends users to attacker-controlled destinations without their knowledge.Attackers target the lack of authentication and integrity checks in traditional DNS protocols. DNS uses UDP without built-in verification, making it vulnerable to forged responses. Attackers send fake DNS responses that beat legitimate ones to the resolver, exploiting prediction patterns and race conditions.Common attack methods include man-in-the-middle attacks that intercept and alter DNS queries, compromising authoritative name servers to modify records directly, and exploiting open DNS resolvers that accept queries from any source.The risks of DNS cache poisoning extend beyond simple redirects. Attackers can steal login credentials by sending users to fake banking sites, distribute malware through poisoned domains, or conduct large-scale phishing campaigns. DNS cache poisoning attacks accounted for over 15% of DNS-related security incidents reported in 2024.Understanding DNS cache poisoning matters because DNS forms the foundation of internet navigation. A single poisoned resolver can affect thousands of users. Poisoned cache entries can persist for hours or days, depending on TTL settings.What is DNS cache poisoning?DNS cache poisoning is a cyberattack where attackers inject false DNS data into a DNS resolver's cache. This redirects users to malicious IP addresses instead of legitimate ones.The attack exploits a fundamental weakness in traditional DNS protocols that use UDP without authentication or integrity checks. This makes it easy for attackers to forge responses.When a DNS resolver's cache is poisoned, it returns incorrect IP addresses to everyone querying that resolver. This can affect thousands of people at once. The problem continues until the corrupted cache entries expire or administrators detect and fix it.How does DNS cache poisoning work?DNS cache poisoning works by inserting false DNS records into a resolver's cache. This causes the resolver to return incorrect IP addresses that redirect users to malicious sites. The attack exploits a fundamental weakness: traditional DNS uses UDP without verifying response integrity or source legitimacy.When your device queries a DNS resolver for a domain's IP address, the resolver caches the answer to speed up future lookups. Attackers inject forged responses into this cache, replacing legitimate IP addresses with malicious ones.The most common method is a race condition exploit. An attacker sends thousands of fake DNS responses with guessed transaction IDs, racing to answer before the legitimate server does. If the forged response arrives first with the correct ID, the resolver accepts and caches it.Man-in-the-middle attacks offer another approach. Attackers intercept DNS queries between clients and servers, then alter responses in transit. They can also directly compromise authoritative name servers to modify DNS records at the source, affecting all resolvers that query them.Open DNS resolvers present particular risks. They accept queries from anyone and can be exploited to poison caches or amplify attacks against other resolvers.A single poisoned cache entry can affect thousands of users simultaneously until the TTL expires. This is especially dangerous on popular public resolvers or ISP DNS servers.What are the main DNS cache poisoning attack methods?Race condition exploits: Attackers send forged DNS responses faster than legitimate authoritative servers can reply. They guess transaction IDs and port numbers to make fake responses look authentic.Man-in-the-middle attacks: Attackers intercept DNS queries between users and resolvers, then modify the responses before they reach their destination. This approach typically targets unsecured network connections such as public Wi-Fi.Authoritative server compromise: Attackers directly access and modify DNS records on authoritative name servers, poisoning DNS data at its source and affecting all resolvers that query the compromised server.Birthday attack technique: Attackers flood resolvers with thousands of forged responses to increase their chances of matching query IDs. The method exploits the limited 16-bit transaction ID space in DNS queries.Open resolver exploitation: Attackers target publicly accessible DNS resolvers that accept queries from any source, poisoning these resolvers to affect multiple downstream users simultaneously.Kaminsky attack: Attackers combine query flooding with subdomain requests to poison entire domain records, sending multiple queries for non-existent subdomains while flooding responses with forged data.What are the risks of DNS cache poisoning?Traffic redirection: Poisoned DNS caches send users to malicious servers instead of legitimate websites, enabling credential theft, malware delivery, and phishing.Man-in-the-middle attacks: Attackers can intercept communications between users and services to steal sensitive information.Widespread user impact: A single compromised resolver can affect thousands or millions of users, especially when large public or ISP DNS servers are poisoned.Credential theft: Victims unknowingly enter login details on fake websites controlled by attackers.Malware distribution: Poisoned records redirect software updates to attacker-controlled servers hosting malicious versions.Business disruption: Organizations lose access to critical services and customer trust until poisoned entries expire.Persistent cache contamination: Malicious records can persist for hours or days depending on TTL values, continuing to infect downstream resolvers.What is a real-world DNS cache poisoning example?In 2023, attackers targeted a major ISP’s DNS resolvers and injected false DNS records that redirected thousands of users to phishing sites. They exploited race conditions by flooding the resolvers with forged responses that arrived faster than legitimate ones. The attack persisted for several hours before detection, compromising customer accounts and demonstrating how a single poisoned resolver can impact thousands of users simultaneously.How to detect DNS cache poisoningYou detect DNS cache poisoning by monitoring DNS query patterns, validating responses, and checking for suspicious redirects across your DNS infrastructure.Monitor resolver logs for unusual query volumes, repeated lookups, or mismatched responses. Set automated alerts for deviations exceeding 20–30% of normal baselines.Enable DNSSEC validation to verify cryptographic signatures on DNS responses and reject tampered data.Compare DNS responses across multiple resolvers and authoritative servers to identify inconsistencies.Analyze TTL values for anomalies; poisoned entries often have irregular durations.Check for SSL certificate mismatches that indicate redirection to fake servers.Use tools like DNSViz to test resolver vulnerability to known poisoning techniques.How to prevent DNS cache poisoning attacksDeploy DNSSEC on authoritative servers and enable validation on resolvers to cryptographically verify responses.Use trusted public DNS resolvers with built-in security validation.Enable source port randomization to make guessing query parameters significantly harder for attackers.Close open resolvers and restrict responses to trusted networks only.Keep DNS software updated with the latest security patches.Set shorter TTL values (300–900 seconds) for critical DNS records to limit exposure duration.Continuously monitor DNS traffic for anomalies and use IDS systems to flag suspicious response patterns.What is the role of DNS service providers in preventing cache poisoning?DNS service providers play a critical role in preventing cache poisoning by validating DNS responses and blocking forged data. They deploy DNSSEC, source port randomization, and rate limiting to make attacks impractical.Secure providers validate response data against DNSSEC signatures, implement 0x20 encoding for query entropy, and monitor for patterns that indicate poisoning attempts. Many also use threat intelligence feeds to block known malicious domains and IPs.Providers that fully implement DNSSEC validation can eliminate forged data injections entirely. Query randomization raises the difficulty of successful poisoning from thousands to millions of attempts, while shorter TTLs and anycast routing further reduce attack windows.However, not all DNS providers maintain equal protection. Open resolvers and outdated configurations remain vulnerable, exposing users to cache poisoning risks.Frequently asked questionsWhat's the difference between DNS cache poisoning and pharming?DNS cache poisoning manipulates a resolver's cache to redirect users to malicious IPs, while pharming more broadly refers to redirecting users to fake sites via DNS poisoning or local malware that modifies host files.How long does DNS cache poisoning last?It lasts until the poisoned record's TTL expires—typically from a few minutes to several days. Administrators can flush caches manually to remove corrupted entries sooner.Can DNS cache poisoning affect mobile devices?Yes. Mobile devices using vulnerable resolvers through Wi-Fi or mobile networks face the same risks, as the attack targets DNS infrastructure rather than device type.Is HTTPS enough to protect against DNS cache poisoning?No. The attack occurs before an HTTPS connection is established, redirecting users before encryption begins.How common are DNS cache poisoning attacks?They’re relatively rare but remain persistent. Over 30% of DNS resolvers worldwide were still vulnerable in 2025, and these attacks accounted for more than 15% of DNS-related security incidents in 2024.Does clearing my DNS cache remove poisoning?Yes. Clearing your local DNS cache removes poisoned entries from your system but won’t help if the upstream resolver remains compromised.
What is a DNS flood attack?
A DNS flood is a type of Distributed Denial of Service (DDoS) attack that overwhelms DNS servers with massive volumes of queries, exhausting server resources and causing service disruption or complete outage for legitimate users. DNS-based attacks accounted for over 20% of all DDoS attacks in 2024, making them one of the most common threats to internet infrastructure.The mechanics are straightforward. DNS flood attacks rely on botnets (networks of compromised devices) that generate enormous traffic volumes. Attackers often use IP address spoofing to mask the true source of queries. This makes it extremely difficult to distinguish legitimate requests from malicious ones.The numbers are significant. The average size of DNS flood attacks has increased to over 50 Gbps in 2024, with some exceeding one terabit per second (Tbps).DNS flood attacks come in several distinct forms, each targeting different aspects of DNS infrastructure. These variations include direct attacks on authoritative name servers, recursive resolver floods, and amplification attacks that exploit DNS protocol features. Understanding these attack types helps organizations build appropriate defenses.The impact extends far beyond the targeted DNS server itself.When a DNS server goes down, every website, application, and service that depends on it for name resolution becomes inaccessible to users. Over 60% of organizations experienced at least one DNS-based DDoS attack in the past 12 months, affecting business operations, revenue, and customer trust.DNS floods pose a significant threat to internet availability because they target critical infrastructure that nearly all online services rely on. A successful attack can take down entire networks, affecting thousands of websites and services simultaneously.What is a DNS flood attack?A DNS flood attack is a type of Distributed Denial of Service (DDoS) attack that overwhelms DNS servers with a massive volume of DNS queries, exhausting server resources and causing service disruption or complete outage for legitimate users. Attackers typically deploy botnets (networks of compromised devices) to generate the high volume of traffic needed to flood the target DNS server, often using IP address spoofing to make it difficult to distinguish between legitimate and malicious traffic. The attack exhausts the DNS server's CPU, memory, and bandwidth. This leads to slow response times or total unavailability of DNS resolution services. The impact extends beyond the targeted DNS server. Any services or websites that rely on it for name resolution can experience widespread internet service disruption.How does a DNS flood attack work?A DNS flood attack works by overwhelming a DNS server with an enormous volume of DNS queries, thereby exhausting its resources and preventing it from responding to legitimate requests. Attackers typically use botnets (networks of compromised computers and IoT devices) to generate millions of queries per second directed at the target DNS server. The flood consumes the server's CPU, memory, and bandwidth, causing slow response times or complete failure.Many attackers spoof IP addresses to conceal their source and make the traffic appear legitimate, making filtering difficult. The attack doesn't just affect the DNS server itself. It disrupts any website or service that depends on that server for name resolution, potentially taking down entire online platforms.DNS flood attacks come in several forms. Standard query floods bombard the server with valid DNS requests for real domains.NXDOMAIN attacks (also called DNS Water Torture) target non-existent domains, forcing the server to waste resources searching for records that don't exist. DNS response floods send fake responses to queries the server never made, clogging its processing queue. Each type aims to exhaust different server resources, but all share the same goal: making DNS resolution unavailable.The attack's impact extends beyond the immediate target. When DNS fails, users can't access websites even though the web servers themselves remain operational.What are the different types of DNS flood attacks?DNS flood attacks use different methods to overwhelm DNS servers with excessive traffic. Here are the main types.DNS query flood: Attackers send massive volumes of legitimate-looking DNS queries to the target server. This exhausts its processing capacity and bandwidth. These queries often target real domain names to make the traffic appear genuine, so the server becomes unable to respond to legitimate user requests as it struggles to process the flood.DNS response flood: Malicious actors spoof the target's IP address and send queries to many DNS servers. Those servers then flood the victim with responses. This amplifies the attack volume because DNS responses are typically larger than queries, meaning the target receives overwhelming traffic without having to query any servers directly.NXDOMAIN attack: Also called DNS water torture, this method floods servers with queries for non-existent domain names. The server must perform full recursive lookups for each fake domain. This consumes a significant amount of CPU and memory resources. It's particularly effective because it bypasses cache mechanisms.Random subdomain attack: Attackers generate queries for random subdomains of a legitimate domain. This forces the authoritative DNS server to respond to each unique request. The randomization prevents caching from reducing the load, which can take down specific domain DNS infrastructure rather than public resolvers.Phantom domain attack: The attacker sets up multiple "phantom" DNS servers that respond slowly or not at all. They then flood the target resolver with queries for domains hosted on these servers. The resolver waits for responses that never arrive, tying up resources and creating a backlog that prevents processing of legitimate queries.Domain lock-up attack: Similar to phantom domain attacks, this method exploits slow DNS responses by creating domains that respond just slowly enough to keep connections open. The target resolver maintains numerous open connections, waiting for responses, which can exhaust connection pools and memory resources.What are the impacts of DNS flood attacks?The impacts of DNS flood attacks refer to the consequences organizations and users experience when DNS servers are overwhelmed by malicious traffic. The effects of DNS flood attacks are listed below.Service unavailability: DNS flood attacks prevent legitimate users from accessing websites and online services by exhausting server resources. When DNS servers can't resolve domain names to IP addresses, all dependent services become unreachable.Revenue loss: Organizations experience direct financial damage when customers are unable to complete transactions during an attack. E-commerce platforms can lose thousands to millions in sales per hour of downtime, especially during peak business periods.Degraded performance: Even when services remain partially available, DNS resolution delays result in slow page loads and a poor user experience. Response times can increase from milliseconds to several seconds, frustrating users and damaging your brand reputation.Resource exhaustion: The attack consumes server CPU, memory, and bandwidth, preventing your infrastructure from handling legitimate queries. This exhaustion affects not just the targeted DNS server but also upstream network equipment and related systems.Widespread cascading failures: DNS flood attacks impact every service that depends on the targeted DNS infrastructure for name resolution. A single compromised DNS provider can simultaneously disrupt access to hundreds or thousands of websites and applications.Increased operational costs: Organizations must invest in mitigation services, additional bandwidth, and incident response efforts during and after attacks. These unplanned expenses include emergency staffing, forensic analysis, and infrastructure upgrades aimed at preventing future incidents.Detection challenges: IP address spoofing makes it difficult to distinguish malicious traffic from legitimate queries, complicating defense efforts. Security teams struggle to implement effective filtering without blocking real users.How to detect a DNS flood attackYou detect a DNS flood attack by monitoring DNS traffic patterns, analyzing query volumes and types, and identifying anomalies that indicate malicious activity targeting your DNS infrastructure.First, establish baseline metrics for your normal DNS traffic patterns over at least 30 days. Track queries per second (QPS), response times, query types, and source IP distributions. This shows you what's typical for your environment.Next, deploy real-time monitoring tools that track DNS query rates and alert you when traffic exceeds your baseline by 200-300% or more. Sudden QPS spikes often signal the start of a flood attack, especially when server performance degrades simultaneously.Then, analyze the distribution of query types in your traffic. DNS flood attacks often show abnormal patterns. You'll see an unusually high percentage of A or AAAA record queries, or a surge in NXDOMAIN responses indicating queries for non-existent domains (Water Torture attacks).Check for signs of IP address spoofing by examining the geographic distribution and diversity of source IPs. Attacks typically involve requests from thousands of different IP addresses across unusual locations. These often exhibit randomized or sequential patterns that don't align with legitimate user behavior.Monitor your DNS server's resource consumption, including CPU usage, memory allocation, and network bandwidth. A flood attack pushes these metrics toward capacity limits (80-100% utilization) even when legitimate traffic hasn't increased proportionally.Look for repetitive query patterns or identical queries from multiple sources. Attackers often send the same DNS queries repeatedly or target specific domains. This creates recognizable signatures in your logs that differ from organic user requests.Finally, track response times and error rates for DNS resolution. When legitimate queries start timing out or your server returns SERVFAIL responses due to resource exhaustion, you're likely experiencing an active attack that requires immediate mitigation. Set up automated alerts that trigger when multiple indicators occur simultaneously. High QPS combined with elevated NXDOMAIN rates and CPU spikes means you need to catch attacks within the first few minutes.How to prevent and mitigate DNS flood attacksYou prevent and mitigate DNS flood attacks by combining proactive defenses, such as rate limiting and traffic filtering, with reactive measures, including anycast routing and DDoS mitigation services.First, deploy rate limiting on your DNS servers to restrict the number of queries from a single IP address within a specific timeframe. Set thresholds based on your normal traffic patterns (typically 5-10 queries per second per IP for most environments) to block excessive requests while allowing legitimate traffic through.Next, configure response rate limiting (RRL) to control the number of identical responses your DNS server sends to the same client. This prevents attackers from exhausting your bandwidth with repetitive queries. It also reduces the effectiveness of amplification techniques.Then, set up anycast routing to distribute DNS queries across multiple geographically dispersed servers. When one location experiences a flood, traffic automatically routes to other servers. This prevents a single point of failure and absorbs attack traffic across your network.After that, enable DNS query filtering to identify and block suspicious patterns, such as NXDOMAIN attacks, which target non-existent domains. Monitor for sudden spikes in queries for domains that don't exist in your zone. These attacks are designed to exhaust server resources through cache misses.Deploy dedicated DDoS mitigation services that can absorb large-scale attacks before they reach your infrastructure. These services typically handle attacks exceeding 50 Gbps and can scrub malicious traffic while forwarding legitimate queries to your DNS servers.Implement DNSSEC to authenticate DNS responses and prevent cache poisoning attempts that often accompany flood attacks. DNSSEC doesn't stop floods directly, but it protects data integrity during attack mitigation efforts.Finally, maintain excess capacity in your DNS infrastructure by provisioning servers with 3-5 times your normal peak load. This buffer gives you time to activate mitigation measures before service degradation occurs. Monitor your DNS traffic continuously with automated alerts for unusual query volumes or patterns. Early detection can reduce the impact of an attack from hours to minutes.What is the difference between DNS floods and other DDoS attacks?A DNS flood attack is a specific type of DDoS attack that targets DNS infrastructure by overwhelming DNS servers with massive volumes of queries. Other DDoS attacks target different layers, such as application servers, network bandwidth, or transport protocols.The key difference lies in the attack vector. DNS floods focus on exhausting DNS server resources (CPU, memory, bandwidth) through query or response floods. Other DDoS attacks might target web servers with HTTP requests, network infrastructure with volumetric attacks, or application logic with sophisticated exploits.DNS floods present unique challenges. Attackers often spoof IP addresses and utilize botnets to generate legitimate-looking DNS queries, making it more challenging to distinguish malicious traffic from normal DNS resolution requests. Other DDoS attacks such as SYN floods, UDP floods, or HTTP floods, work at different network layers and require different detection and mitigation approaches.Frequently asked questionsWhat's the difference between a DNS flood and a DNS amplification attack?A DNS flood overwhelms DNS servers with massive query volumes, exhausting their resources. DNS amplification works differently. It exploits open DNS resolvers to multiply attack traffic and redirect it toward a target. DNS floods rely on sheer volume from botnets to take down servers. Amplification attacks turn legitimate DNS servers into unwitting participants that send larger responses to a spoofed victim address, magnifying the impact of each request.How long does a typical DNS flood attack last?DNS flood attacks typically last from a few minutes to several hours. Some sophisticated campaigns persist for days with intermittent bursts. Attack duration depends on three key factors: the attacker's resources, their objectives, and how quickly you deploy effective mitigation measures.Can small businesses be targets of DNS flood attacks?Yes, small businesses are targets of DNS flood attacks. Attackers often view them as easier targets with weaker defenses than those of large enterprises.What is the cost of DNS flood protection services?DNS flood protection costs range from a free basic mitigation to over $1,000 per month for enterprise solutions. Pricing depends on your traffic volume, the scale of attacks you need to handle, and the features you select (such as always-on protection versus on-demand activation).How does DNS caching help against flood attacks?DNS caching helps protect against flood attacks by storing query responses locally, which cuts the load on authoritative DNS servers. This means recursive DNS servers can answer repeated queries directly from cache without forwarding traffic to your overwhelmed target server. Cached responses continue serving legitimate requests even during an active attack.Are cloud-based DNS services more resistant to floods?Yes, cloud-based DNS services are significantly more resistant to floods. They distribute traffic across multiple global servers and can absorb attack volumes that would overwhelm a single infrastructure. They typically offer automatic scaling and traffic filtering that detects and blocks malicious queries in real time, often mitigating attacks within minutes.What should I do during an active DNS flood attack?Contact your DNS provider or managed security service right away to enable rate limiting and traffic filtering at the network edge. If you manage your own DNS infrastructure, here's what you need to do: activate DDoS mitigation tools, temporarily increase server capacity, and implement query rate limits per source IP. This approach blocks malicious traffic while allowing legitimate requests to pass through.
What are WAF policies and how do they protect web applications?
A WAF policy is a set of rules that defines how a Web Application Firewall inspects incoming web traffic and what actions to take (allow, block, challenge, or log) based on detected threats or patterns. Over 80% of web applications are vulnerable to at least one of the OWASP Top 10 security risks. These policies are crucial for protecting against common exploits, such as SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF).WAF policies work by filtering HTTP/HTTPS traffic to and from web applications and APIs. They act as a reverse proxy between users and web servers. The firewall examines each request against configured rules to identify malicious behavior, such as known attack signatures or abnormal request sizes and frequencies.When properly configured, modern WAFs can reduce successful web attacks by up to 90%.WAF policy rules fall into three main categories that determine their security approach. Blocklist rules (negative security model) block known malicious traffic patterns. Allowlist rules (positive security model) only permit pre-approved traffic. Hybrid models combine both approaches to balance security and flexibility, giving you more control over how traffic is filtered.Creating a WAF policy involves selecting deployment options and configuring rule sets for your specific needs.You can deploy WAFs as network-based, host-based, or cloud-based solutions. Each option offers different benefits for traffic inspection and filtering. You'll need to define which rules to apply, set thresholds for anomaly detection, and determine response actions for different threat types.WAF adoption in enterprises increased by approximately 25% from 2023 to 2025. This reflects the growing importance of web application security. As web-based attacks continue to grow in volume and complexity, implementing effective WAF policies has become a core requirement for protecting business-critical applications and sensitive data.What is a WAF policy?A WAF policy is a set of rules that defines how a Web Application Firewall inspects incoming HTTP/HTTPS traffic and determines what actions to take, such as allowing, blocking, challenging, or logging requests based on detected threats or patterns. These policies analyze web requests against configured rules to identify malicious behavior, such as SQL injection, cross-site scripting (XSS), or abnormal request patterns. They then enforce security actions to protect your applications. Modern WAF policies employ three primary approaches: blocklist rules that deny known malicious traffic, allowlist rules that permit only pre-approved traffic, or hybrid models that combine both methods for balanced protection.How does a WAF policy work?A WAF policy works by defining a set of rules that analyze incoming HTTP/HTTPS requests and determine whether to allow, block, challenge, or log each request based on detected threat patterns. When traffic reaches the WAF, it inspects request elements, such as headers, query strings, request bodies, and HTTP methods, against configured rules. The policy compares this data to known attack signatures and behavioral patterns to identify threats, such as SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF).WAF policies work through three main rule types.Blocklist rules (negative security model) deny traffic matching known malicious patterns, such as specific SQL injection strings or suspicious user agents. Allowlist rules (positive security model) only permit pre-approved traffic that meets exact criteria, blocking all other traffic by default. Hybrid models combine both approaches. They use allowlists for critical application paths while applying blocklists to detect new threats.The WAF typically sits as a reverse proxy between users and your web servers, inspecting every request before it reaches the application.When a request matches a rule, the policy executes the defined action immediately. Modern WAFs can process these checks in milliseconds, analyzing multiple rule sets simultaneously without noticeable latency. You can customize policies by adding exceptions for legitimate traffic that triggers false positives, adjusting sensitivity levels, and creating custom rules specific to your application's security needs.What are the main types of WAF policy rules?WAF policy rules define how a Web Application Firewall inspects, filters, and responds to incoming web traffic based on your security requirements. Here are the main types you'll encounter.Blocklist rules: These follow a negative security model by identifying and blocking known malicious traffic patterns. The WAF maintains a database of harmful requests (like SQL injection attempts or cross-site scripting payloads) and denies any traffic matching these patterns.Allowlist rules: These implement a positive security model that only permits pre-approved, legitimate traffic to reach your web application. All requests must match specific criteria such as approved IP addresses, user agents, or request formats. The WAF blocks everything else by default.Hybrid rules: These combine both blocklist and allowlist approaches to balance security and usability. You can block known threats while allowing verified legitimate traffic, providing flexible protection that adapts to different application needs.Rate limiting rules: These monitor and control the frequency of requests from specific sources to prevent abuse and denial-of-service attacks. The WAF tracks request rates per IP address or user session and blocks or throttles traffic exceeding your defined thresholds.Geolocation rules: These filter traffic based on the geographic origin of requests. You can block or allow access from specific countries or regions, which helps prevent attacks from known malicious locations while maintaining access for legitimate users.Custom signature rules: These define organization-specific patterns and conditions tailored to protect unique application vulnerabilities or business logic. Security teams create custom detection patterns that address threats specific to their web applications, going beyond standard managed rules.Behavioral analysis rules: These examine traffic patterns and user behavior over time to detect anomalies that might indicate attacks or unauthorized access. The WAF establishes a baseline behavior and flags deviations, such as unusual request sequences or abnormal data access patterns.How to create a WAF policyYou create a WAF policy by defining security rules that determine how your WAF inspects incoming web traffic and responds to potential threats.Assess your web application's architecture. Identify the components that require protection, including APIs, login pages, payment forms, and data submission endpoints. Document the HTTP methods your application uses (GET, POST, PUT, DELETE) and any custom headers or parameters.Select your base ruleset. Choose between three security models: blocklist rules that deny known malicious patterns, such as SQL injection signatures; allowlist rules that permit only pre-approved traffic sources and patterns; or a hybrid approach combining both. Most organizations begin with managed rule sets covering OWASP Top 10 vulnerabilities.Configure rule actions for different threat levels. Set responses like block (reject the request), allow (permit the traffic), challenge (require CAPTCHA verification), or log (record for analysis without blocking). Assign stricter actions to high-risk endpoints such as admin panels.Add custom rules tailored to your application. Examples include rate limiting to prevent brute force attacks (limit login attempts to 5 per minute per IP), geographic restrictions to block traffic from regions you don't serve, or pattern matching for application-specific attack vectors.Define exclusions for legitimate traffic. Some valid requests might trigger false positives. Allow large file uploads for authenticated users or permit specific API clients to bypass certain inspection rules. Test these exclusions carefully to avoid creating security gaps.Configure logging and monitoring settings. Capture blocked requests, suspicious patterns, and policy violations. Set alert thresholds for unusual traffic spikes or attack patterns that exceed normal baselines by 200% or more.Test your WAF policy in detection-only mode. Run it for 7 to 14 days before enabling blocking actions. Review logs daily during this period to identify false positives and adjust rules to strike a balance between security and application availability.Start with managed rulesets and add custom rules gradually. Base your configuration on your application's traffic patterns and security requirements rather than trying to configure everything at once.How to configure WAF policy protectionsYou configure WAF policy protections by defining security rules that analyze incoming web traffic, setting appropriate actions for detected threats, and tailoring protections to match your application's specific security needs.First, access your WAF management interface and create a new policy by specifying its name, scope, and enforcement mode (detection only or prevention). Start in detection mode to monitor traffic patterns for 7-14 days without blocking requests. This helps identify legitimate traffic and reduces the number of false positives.Next, enable managed rule sets that provide pre-configured protections against common web attacks, such as SQL injection, cross-site scripting (XSS), and remote file inclusion. Security experts maintain and update these rules regularly to address new threats, automatically covering most OWASP Top 10 vulnerabilities.Then, configure your security model by choosing between blocklist rules that deny known malicious patterns, allowlist rules that permit only approved traffic sources, or a hybrid approach combining both methods. Blocklist works well for public-facing sites. Allowlist suits applications with predictable user behavior.After that, set specific rule actions for different threat levels: block high-severity attacks immediately, challenge medium-severity requests with CAPTCHA verification, and log low-severity events for review. This tiered approach strikes a balance between security and user experience, preventing legitimate users from being blocked unnecessarily.Create custom rules to address application-specific vulnerabilities or business logic requirements that managed rules don't cover. For example, you might block requests exceeding certain parameter lengths, restrict access to admin endpoints by IP address, or enforce rate limits on API calls.Configure rule exclusions for legitimate traffic that triggers false positives, such as rich text editors that submit HTML content or file upload features that send large POST requests. Document each exclusion with a clear business justification to maintain security visibility and transparency.Finally, enable logging and monitoring to track blocked requests, review security events, and analyze attack patterns. Set up alerts for unusual activity spikes or repeated attack attempts from specific sources so your team can respond quickly.Test your WAF policy in staging environments with realistic traffic before applying it to production. Review security logs weekly during the first month to fine-tune rules and reduce false positives.What are WAF policy best practices?WAF policy best practices refer to the recommended methods and strategies for configuring and managing Web Application Firewall rules to maximize security effectiveness while minimizing operational disruption. Here are the key WAF policy best practices.Start with managed rules: Managed rule sets provide pre-configured protections against OWASP Top 10 vulnerabilities and common attack patterns. You don't need deep security expertise to use them. These rules receive regular updates from security vendors to address emerging threats. Most organizations can block 70-80% of attacks using managed rules alone.Implement logging before blocking: Deploy new WAF rules in detection or log-only mode first. This lets you monitor their impact on legitimate traffic before enforcing blocks. You'll identify false positives and fine-tune rules without disrupting user access. After 7-14 days of monitoring, you can switch to blocking mode with confidence.Create custom rules for your application: Build application-specific rules that address unique security requirements and business logic vulnerabilities that generic rules can't protect. Custom rules can target specific URL paths, API endpoints, or user behaviors unique to your application. These tailored protections often catch threats that managed rules miss.Use rate limiting strategically: Configure rate limits on login pages, API endpoints, and resource-intensive operations to prevent brute force attacks and DDoS attempts. Set thresholds based on normal traffic patterns, such as 100 requests per minute per IP address. Rate limiting protects application availability without blocking legitimate users.Tune rules to reduce false positives: Regularly review blocked requests to identify legitimate traffic incorrectly flagged as malicious, then adjust rules or add exceptions. High false positive rates create security team fatigue and may lead to turning off important protections. Aim to keep false positive rates below 5% through continuous tuning.Apply the principle of least privilege: Configure allowlist rules for known good traffic sources and user agents when possible, blocking all other traffic by default. This positive security model provides stronger protection than blocklist approaches for high-security applications. It's particularly effective for internal applications with predictable access patterns.Monitor and update policies regularly: Review WAF logs weekly to identify attack trends, rule effectiveness, and potential policy gaps. Update rules monthly or when new vulnerabilities emerge in your application stack. Regular maintenance ensures that protections remain aligned with evolving threats and changes to applications.How to troubleshoot common WAF policy issuesTroubleshoot common WAF policy issues by checking rule configurations, analyzing traffic logs, adjusting sensitivity settings, and testing policies in monitor mode before enforcing blocks.Start by reviewing your WAF logs to identify false positives or blocked legitimate traffic. Look for patterns in blocked requests, like specific IP addresses, user agents, or request paths that shouldn't trigger blocks. Most false positives happen when legitimate requests match overly strict rule patterns.Verify that your WAF rules do not conflict with your application's normal behavior. Test API calls, form submissions, and file uploads in a staging environment to identify which rules are triggered incorrectly. Common issues include blocking legitimate POST requests with large payloads or flagging standard authentication headers.Adjust rule sensitivity by creating custom exclusions for specific application paths or parameters. If a rule blocks legitimate traffic to /api/upload, add an exclusion for that endpoint while keeping protection active elsewhere. This maintains security without disrupting functionality.Verify that your allowlist and blocklist rules don't contradict each other. A common mistake is having an allowlist rule that permits traffic, while a blocklist rule blocks, creating unpredictable behavior. Review rule priority and execution order to ensure the most specific rules process first.Test policy changes in monitor mode before switching to block mode. Monitor mode logs potential threats without blocking them, letting you validate that new rules won't disrupt legitimate traffic. Run monitor mode for 24 to 48 hours to capture typical traffic patterns across different time zones.Check if geographic restrictions or rate-limiting rules are too aggressive. If users from specific regions report access issues, verify your geo-blocking rules. If legitimate users hit rate limits, increase thresholds, or implement more granular rate limiting per endpoint rather than globally.Review managed rule sets for recent updates that might affect your application. WAF providers regularly update managed rules to address new threats, but these updates can sometimes flag previously allowed traffic. Roll back recent rule changes if issues started after an update.Keep detailed documentation of all policy changes and their effects. This speeds up future troubleshooting and helps your team understand why specific exclusions are in place.What are the differences between WAF policy deployment options?Organizations can implement Web Application Firewall solutions in several distinct ways, each with its own unique hosting and deployment characteristics. Here are the main WAF policy deployment options.Network-based WAF: Hardware appliances installed on-premises within your data center or network perimeter. The appliance sits between external users and web servers as a reverse proxy, inspecting all HTTP/HTTPS traffic before it reaches your applications. This option delivers low latency and high performance. However, it requires significant upfront capital investment and ongoing maintenance.Host-based WAF: WAF software installed directly on the web server or application server hosting your protected application. The WAF runs as a module or service on the same machine, inspecting traffic at the application layer without separate hardware. This approach offers deep integration with your application, but it consumes server resources and requires individual configuration for each host.Cloud-based WAF: WAF protection delivered as a service through cloud infrastructure, with traffic routing through the provider's edge network before reaching your origin servers. You'll configure policies through a web interface, eliminating the need to manage physical or virtual appliances. Cloud-based WAFs offer rapid deployment, automatic updates, and elastic scaling. The tradeoff is that all traffic routes through external infrastructure.Hybrid deployment: This approach combines multiple deployment models. For example, you might use cloud-based WAF for public-facing applications while maintaining network-based appliances for internal or legacy systems. Organizations can balance performance, security, and cost requirements across different application environments. Hybrid models provide flexibility but increase management complexity across multiple platforms.API-integrated WAF: WAF protection connected through API gateways or service mesh architectures in microservices environments. The WAF inspects API calls and responses at the gateway layer, applying policies specific to REST, GraphQL, or SOAP protocols. This deployment works well for modern application architectures but requires careful configuration to avoid breaking legitimate API functionality.Container-based WAF: WAF protection deployed as containerized workloads within Kubernetes or similar orchestration platforms. The WAF runs as a sidecar container alongside application containers, inspecting traffic within the cluster. Container-based deployments offer portability and integration with DevOps workflows. You'll need container expertise and proper resource allocation to implement this option effectively.Frequently asked questionsWhat's the difference between a WAF policy and a security policy?A WAF policy defines the complete ruleset that governs how a Web Application Firewall inspects and responds to web traffic. A security policy is broader. It's an organizational framework that covers all security controls and procedures.WAF policies contain specific technical rules (blocklist, allowlist, or hybrid) that detect attack patterns, such as SQL injection or XSS. Security policies work differently. They document high-level requirements, compliance standards, and access controls across your entire infrastructure.How many rules should a WAF policy contain?There's no fixed number of WAF policy rules. The right amount depends on the complexity of your application, its traffic patterns, and your security needs.Most organizations begin with 10-20 managed rules that cover OWASP Top 10 threats, then add 5-15 custom rules for application-specific protections.Can I use multiple WAF policies simultaneously?No, you can't apply multiple WAF policies to a single web application or endpoint at once. Each resource accepts only one active policy at a time.However, you can build a single comprehensive policy that combines multiple rule sets, managed rules, and custom rules. This approach provides layered protection without requiring multiple policies.What happens when a WAF policy blocks legitimate traffic?When a WAF policy blocks legitimate traffic (known as a false positive), users are unable to access your web application or specific features. This means your security team needs to step in and adjust the rules.To address false positives, you typically create exceptions or adjust sensitivity thresholds. Most organizations maintain detailed logs to spot these issues quickly. You can then refine your WAF policies through allowlist rules or custom exclusions that permit known safe traffic patterns.How often should I update my WAF policy?Review your WAF policy at least once a month to ensure it remains effective. Update it immediately when new vulnerabilities emerge, your application changes, or you notice shifting attack patterns. Audit rule effectiveness every quarter and adjust false positive thresholds based on traffic analysis. This keeps your protection strong without blocking legitimate users.Does a WAF policy impact website performance?Yes, a WAF policy typically adds minimal latency, ranging from 1 to 5 milliseconds per request. This overhead is negligible compared to the security benefits it provides.What's the difference between detection mode and prevention mode?Detection mode monitors and logs suspicious traffic without blocking it. Prevention mode actively blocks threats in real-time.We recommend using detection mode when you're testing WAF rules before enforcement. This approach helps you avoid accidentally disrupting legitimate traffic while you fine-tune your security configuration.
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