A bot, short for “robot,” is a software program that can perform tasks automatically, quickly, and efficiently. Both good bots and bad bots exist; Googlebot facilitates web page indexing, but LizardStresser orchestrates DDoS attacks. Because good and bad bots share certain traits, distinguishing between them can be tricky unless the correct bot detection techniques are used. In this article, we examine the evolution of bot detection techniques in response to the ever-changing threat landscape and discuss how bots can be detected and, when desirable, stopped.
What Is Bot Detection?
Bot detection is the process of identifying and distinguishing between legitimate human users, good bots, and bad bots. Because bots can mimic certain legitimate user behaviors, such as mouse movements and keystrokes, cybersecurity professionals and business leaders should implement bot detection as an integral component of their security strategy. Otherwise, you could end up with misleading analytics, compromised user experiences, and potential security breaches that can harm your organization’s reputation and bottom line.
Bot detection helps to mitigate malicious bot activities such as unethical web scraping, spamming, account takeover, click fraud, and DDoS attacks, without interfering with good bots such as website uptime monitors. Effective bot detection enhances cybersecurity and improves the web user’s overall experience.
Botnet Detection Techniques
Over the decades, different botnet mitigation techniques have been developed to deal with the challenges of stopping bad bots while allowing good bots to continue their activities. These techniques typically involve identifying the command-and-control infrastructure coordinating the botnet activities. However, since botnets keep evolving to bypass mitigation measures, new and better botnet detection and mitigation strategies are continuously being developed.
Let’s examine botnet detection techniques. We’ll start with the oldest and then look at contemporary techniques. However, new techniques build on the old, and all these techniques still play a part in botnet detection today.
Intrusion Detection Systems
Intrusion detection systems (IDS) emerged in the late 1980s to monitor and analyze network traffic for security incidents like unauthorized access and policy violations. IDS can detect threats, such as botnets, and alert security teams. Intrusion prevention systems (IPS) can proactively mitigate detected threats. Modern IDPS (intrusion detection and prevention systems) combine IDS and IPS functions.
IDS is trained on data from sources like network traffic, system logs, and application activity. Botnet-focused IDS can be anomaly-based (monitoring abnormal behaviors) or signature-based (matching patterns with known botnets).
When a potential botnet is detected, the IDS generates alerts or notifications based on severity. Depending on cybersecurity policies, the IDS may block traffic, isolate systems, or alert security teams. IDS also generates incident logs and reports, detailing the time of incidents, detected threats, countermeasures, and recommendations for improvement.
Intrusion detection systems can be grouped into six types:
- Network-Based Intrusion Detection Systems (NIDS): These monitor real-time network traffic and analyze packets on network segments or devices to detect attacks like DoS, port scanning, and reconnaissance.
- Protocol-Based Intrusion Detection Systems (PIDS): A type of NIDS that targets specific network communication protocols (e.g., P2P, HTTP, IRC) to protect against intrusion and policy violations. PIDS is limited in scope.
- Machine Learning-Based Intrusion Detection Systems (ML-IDS): Subset of NIDS using machine learning algorithms to detect network intrusions and malicious activities by learning from historical data. ML-IDS is more efficient than traditional rule-based systems but requires fine-tuning to minimize false positives.
- Host-Based Intrusion Detection Systems (HIDS): Monitor the computer infrastructure they are installed on (e.g., computers, servers) to safeguard against attacks. They gather data, analyze traffic, and log suspicious behavior, providing insights into system health and security. HIDS is an approach that’s most suitable for small teams with lean overheads.
- Hybrid Intrusion Detection Systems: Combine different detection techniques (e.g., NIDS, HIDS, anomaly-based, signature-based) in a single framework to effectively detect botnet activity and provide insightful data. Problematically, they create a single point of failure and are complex to troubleshoot.
- Multi-Layered Intrusion Detection Systems: These systems combine different detection techniques (e.g., NIDS, HIDS, anomaly-based, signature-based) in a layered approach, with each IDS as a separate component. They eliminate a single point of failure and simplify troubleshooting but complicate setup, management, and reporting.
To summarize, intrusion detection systems (IDS) enhance network security by monitoring and analyzing traffic to detect potential threats, providing valuable insights and real-time response capabilities. However, they can produce false positives, require ongoing maintenance and fine-tuning, and may be complex to manage and integrate into existing security frameworks.
Honeynet
First used around the year 2000, a honeynet is a network of traps or decoy networks (honeypots) set up with built-in vulnerabilities to attract cyberattacks. A typical honeynet comprises two or more honeypots. Honeynets aid in botnet detection by deliberately exposing vulnerabilities that attract malicious attacks. This deception technique allows botnet attacks to be studied in a controlled environment or managed and stopped, as needed.
As such, there are two main types of honeynets: research honeynets and production honeynets. Research honeynets are primarily set up to study attack vector tactics, techniques, and procedures, while production honeynets are deployed within production environments.
Despite their effectiveness, honeynets have limitations, such as setup complexity, limited network coverage, and high maintenance overhead, especially for high-capacity setups. Additionally, honeynets can sometimes be detected, bypassed, armed, and deployed against the production network itself.
DNS-Based Botnet Detection
Around 2005, the DNS-based botnet detection technique started to gain popularity. DNS-based botnet detection works by monitoring the way computers use the Domain Name System (DNS) to find websites. When you enter a website address into your browser, your computer uses DNS to find the numerical IP address that corresponds to that website. Botnets, which are networks of infected computers controlled by cybercriminals, often need to communicate with the attackers’ servers to receive instructions. They use DNS to find these servers.
A botnet detection system monitors all DNS requests made by network computers. They analyze which domain names are being requested and how often. Since botnets often use unusual domain names that people don’t typically visit, the systems look for patterns that indicate suspicious activity, such as frequent requests to these strange or newly created domains. They can then block the requests to these malicious domains, preventing the infected computers from communicating with the cybercriminals.
Although they provide real-time detection, network-wide coverage, low false-positive rates, and threat intelligence gathering, they are prone to evasion techniques and are limited by their reliance on external threat intelligence sources for domain reputation data.
Comparison of Botnet Detection Techniques
Here’s how these three botnet detection techniques compare.
| Feature | Intrusion Detection Systems (IDS) | Honeynet | DNS-Based Botnet Detection |
| Definition | Network security tools monitor and analyze network traffic for potential threats | Network of traps or decoy networks designed to attract cyberattacks | Technique monitoring and analyzing DNS traffic for botnet activity |
| Detection focus | Network traffic, system logs, and application activity | Cyberattackers’ behavior and tactics | DNS traffic patterns, requests, and responses |
| Detection methods | Signature-based, anomaly-based, machine learning | Deception through vulnerabilities | Domain reputation checks, anomaly detection |
| Data collected | Network traffic, system logs, application activity | Attack interactions with honeypots | DNS traffic, requests, responses |
| Alerting and response | Generates alerts, blocks traffic, isolates systems | Studies attacks, handles malicious interactions | Blocks connections, redirects to sinkholes, alerts |
| Use cases | Prevents unauthorized access, breaches, policy violations | Studies attack tactics, gathers threat intelligence | Real-time botnet detection, low false positives |
| Complexity | Varies based on IDS type (NIDS, HIDS, hybrid, multi-layered) | Moderate to high due to setup and maintenance | Moderate, relies on DNS traffic analysis |
| Effectiveness | Effective for detecting network-based threats | Effective for studying attacks, gathering threat intel | Effective for real-time botnet detection |
| Limitations | Can be bypassed by sophisticated attacks | Setup complexity, limited network coverage | Prone to evasion techniques, reliance on external data |
| Deployment | Network-wide, host-based, hybrid, multi-layered | Controlled environment, production networks | DNS infrastructure monitoring |
| Popularity | Widely used in cybersecurity | Less common due to complexity | Increasing popularity |
| Future evolution | Evolving to integrate AI, threat intelligence | Evolving to address evasion techniques | Evolving to handle DNS tunneling |
| Management overhead | Varied based on IDS type and deployment | High for setup, maintenance, and monitoring | Moderate for DNS traffic analysis |
How to Stop Botnets
Now we know how undesirable botnets are detected, let’s turn to how they can be stopped. Three main options exist: CAPTCHA, rate limiting, and bot protection.
A. JS Challenges/CAPTCHA
One way to stop bad bot activity is by implementing JS Challenges and CAPTCHA on your websites or web applications. Both are effective security mechanisms used to protect against malicious bots, automated scripts, and other unauthorized automated activities, such as web scraping.
Gcore provides JS Challenge and JS CAPTCHA solutions as part of Gcore WAAP. First, a JS challenge runs a small piece of JavaScript code in the user’s browser, which a bot typically cannot execute. This code checks for typical human behavior and browser characteristics to ensure the request comes from a legitimate user. Next, a CAPTCHA presents a task that is easy for humans but difficult for bots, such as identifying objects in images or solving simple puzzles. By completing these tasks, users prove they are human, thereby preventing automated systems from accessing or abusing web services.
But there’s a downside: CAPTCHAs do not distinguish between beneficial bots (such as search engine crawlers or monitoring tools) and malicious bots. They can impede good bots from performing their intended functions. To allow good bots while still protecting against malicious ones, website administrators need to create exceptions or use alternative verification methods that can recognize and permit trusted bots. Gcore manages this process with our WAAP customers to ensure good bots continue to function effectively.
B. Rate Limiting
A key characteristic of bots is their ability to automate and rapidly scale tasks. For example, bots can fill and submit forms much faster than humans, sending a large number of requests to the server and receiving an equally large number of responses. This can drain server resources and degrade site performance.
Rate limiting controls the number of requests an IP address or IP range can make to a resource within a certain timeframe. This method mitigates bad bot activity on websites or web applications. Good bots don’t engage in this kind of behavior, so there’s not much risk of stopping their activity with a rate limiter.
Gcore Rate Limiter protects your websites and web applications from excessive requests that signal bad bot activity. You can specify a set of rules dictating how many requests are allowed per IP address per second. Once this limit is exceeded, the requester will receive an HTTP 429 (Too Many Requests) error message.
Stop Bad Bots with Gcore WAAP
While bot detection techniques such as honeynets, DNS-based bot detectors, and intrusion detection systems (IDSs) are effective in their own right, a hybrid or multi-layered bot detection approach is the most accurate way to detect bot activity. Gcore WAAP (Web Application Firewall + API Protection) is the ultimate all-in-one bot detection and protection solution for your websites and web applications. Gcore WAAP incorporates bot protection with a web application firewall, API security, and advanced DDoS protection to offer enhanced enterprise-grade security.
We protect against threats including and beyond the OWASP Top 10, addressing unpatched vulnerabilities and zero-day attacks by leveraging machine learning technologies. With Gcore WAAP, you enjoy API-specific protection and security against credential stuffing, account takeover, brute force attacks, and L7 DDoS attacks.
Gcore WAAP is scalable to meet your needs, regardless of industry. It is also easy to deploy—no additional hardware, software, or changes in the code are required on your part. Once you send a request, Gcore will start protecting your web resources immediately. Request Gcore WAAP today and enjoy bot-free websites and web applications.
Conclusion
Detecting and stopping bad bots involves a combination of advanced techniques tailored to identify and mitigate malicious activities while allowing beneficial bots to operate. Implementing a multi-layered bot detection strategy, such as Gcore WAAP, ensures comprehensive protection against various threats while maintaining website performance and user experience.
Gcore WAAP is integrated into Gcore’s global infrastructure, operating on 180+ global points of presence in Tier III and IV data centers, ensuring optimal performance, low latency worldwide, and outstanding security at the network’s edge. Secure your web applications and APIs against the most sophisticated cyber threats to safeguard your business’ reputation.
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Exposed login pages are a common vulnerability across web applications. Attackers often use automated tools to guess credentials in brute-force or credential-stuffing attacks, probe for login behavior to exploit session or authentication logic, or overload your infrastructure with fake requests.Without specific rules for login-related traffic, your application might miss these threats or apply overly broad protections that disrupt real users. Fortunately, Gcore WAAP makes it easy to defend these sensitive endpoints without touching your application code.In this guide, we’ll show you how to use WAAP’s custom rule engine to identify login traffic and apply protections like CAPTCHA to reduce risk, block automated abuse, and maintain a smooth experience for legitimate users. We’ve also included a complete video walkthrough from Gcore’s Security Presales Engineer, Michal Zalewski.Video walkthroughHere’s Gcore’s Michal Zalewski giving a full walkthrough of the steps in this article.Step 1: Access your WAAP configurationGo to portal.gcore.com and log in.Navigate to WAAP in the sidebar. If you’re not yet a WAAP user, it costs just $26/month.Select the resource that hosts your login form; for example, gcore.zalewski.cloud.Step 2: Create a custom ruleIn the main panel of your selected resource, go to WAAP Rules.Click Add Custom Rule in the upper-right corner.Step 3: Define the login page URLIdentify the login endpoint you want to protect:Use tools like Burp Suite or the "Inspect" feature in your browser to verify the login page URL.In Burp Suite, use the Proxy tab, or in the browser, check the Network tab to inspect a login request.Look for the path (e.g., /login.php) and HTTP method (POST).In the custom rule setup:Enter the URL (e.g., /login.php).Tag the request using a predefined tag. Select Login Page.Step 4: Name and save the ruleProvide a name for the rule, such as “Login Page URL”, and save it.Step 5: Add a CAPTCHA challenge ruleTo protect the login page from automated abuse:Create a new custom rule.Name it something like “Login Page Challenge”.Under Conditions, select the previously created Login Page tag.Set the Action to CAPTCHA.Save the rule.Step 6: Test the protectionReturn to your browser and turn off any proxy tools.Refresh the login page.You should now be challenged with a CAPTCHA each time the login page loads.Once the CAPTCHA is completed successfully, users can log in as usual.Monitor, adapt, and alertAfter deployment:Track rate limit trigger frequencyMonitor WAAP logs for anomaly detectionRotate exemptions or thresholds based on live behaviorFor analytics, refer to the WAAP analytics documentation.Bonus tips for hardened protectionCombine with bot protection: Enable WAAP’s bot mitigation to identify headless browsers and automation tools like Puppeteer or Selenium. See our bot protection docs for setup instructions.Customize 429 responses: Replace default error pages with branded messages or a fallback action. Consider including a support link or CAPTCHA challenge. Check out our response pages documentation for more details.Use geo or ASN exceptions: Whitelist trusted locations or block known bot-heavy ASNs if your audience is localized.Automate it: optional API and Terraform supportTeams with IaC pipelines or security automation workflows might want to automate login page protection with rate limiting. This keeps your WAAP config version-controlled and repeatable.You can use the WAAP API or Terraform to:Create or update rulesRotate session keys or thresholdsExport logs for auditingExplore the WAAP API documentation and WAAP Terraform provider documentation for more details.Stop abuse before it starts with GcoreLogin pages are high-value targets, but they don’t have to be high risk. With Gcore WAAP, setting up robust defenses takes just a few minutes. By tagging login traffic and applying challenge rules like CAPTCHA, you can reduce automated attack risk without sacrificing user experience.As your application grows, revisit your WAAP rules regularly to adapt to new threats, add behavior-based detection, and fine-tune your protective layers. For more advanced configurations, check out our documentation or reach out to Gcore support.Get WAAP today for just $26/month
3 underestimated security risks of AI workloads and how to overcome them
Artificial intelligence workloads introduce a fundamentally different security landscape for engineering and security teams. Unlike traditional applications, AI systems must protect not just endpoints and networks, but also training data pipelines, feature stores, model repositories, and inference APIs. Each phase of the AI life cycle presents distinct attack vectors that adversaries can exploit to corrupt model behavior, extract proprietary logic, or manipulate downstream outputs.In this article, we uncover three security vulnerabilities of AI workloads and explain how developers and MLOps teams can overcome them. We also look at how investing in your AI security can save time and money, explore the challenges that lie ahead for AI security, and offer a simplified way to protect your AI workloads with Gcore.Risk #1: data poisoningData poisoning is a targeted attack on the integrity of AI systems, where malicious actors subtly inject corrupted or manipulated data into training pipelines. The result is a model that behaves unpredictably, generates biased or false outputs, or embeds hidden logic that can be triggered post-deployment. This can undermine business-critical applications—from fraud detection and medical diagnostics to content moderation and autonomous decision-making.For developers, the stakes are high: poisoned models are hard to detect once deployed, and even small perturbations in training data can have system-wide consequences. Luckily, you can take a few steps to mitigate against data poisoning and then implement zero-trust AI to further protect your workloads.Mitigation and hardeningRestrict dataset access using IAM, RBAC, or identity-aware proxies.Store all datasets in versioned, signed, and hashed formats.Validate datasets with automated schema checks, label distribution scans, and statistical outlier detection before training.Track data provenance with metadata logs and checksums.Block training runs if datasets fail predefined data quality gates.Integrate data validation scripts into CI/CD pipelines pre-training.Enforce zero-trust access policies for data ingestion services.Solution integration: zero-trust AIImplement continuous authentication and authorization for each component interacting with data (e.g., preprocessing scripts, training jobs).Enable real-time threat detection during training using runtime security tools.Automate incident response triggers for unexpected file access or data source changes.Risk #2: adversarial attacksAdversarial attacks manipulate model inputs in subtle ways that trick AI systems into making incorrect or dangerous decisions. These perturbations—often imperceptible to humans—can cause models to misclassify images, misinterpret speech, or misread sensor data. In high-stakes environments like facial recognition, autonomous vehicles, or fraud detection, these failures can result in security breaches, legal liabilities, or physical harm.For developers, the threat is real: even state-of-the-art models can be easily fooled without adversarial hardening. The good news? You can make your models more robust by combining defensive training techniques, input sanitization, and secure API practices. While encrypted inference doesn’t directly block adversarial manipulation, it ensures that sensitive inference data stays protected even if attackers attempt to probe the system.Mitigation and hardeningUse adversarial training frameworks like CleverHans or IBM ART to expose models to perturbed inputs during training.Apply input sanitization layers (e.g., JPEG re-encoding, blurring, or noise filters) before data reaches the model.Implement rate limiting and authentication on inference APIs to block automated adversarial probing.Use model ensembles or randomized smoothing to improve resilience to small input perturbations.Log and analyze input-output patterns to detect high-variance or abnormal responses.Test models regularly against known attack vectors using robustness evaluation tools.Solution integration: encrypted inferenceWhile encryption doesn't prevent adversarial inputs, it does mean that input data and model responses remain confidential and protected from observation or tampering during inference.Run inference in trusted environments like Intel SGX or AWS Nitro Enclaves to protect model and data integrity.Use homomorphic encryption or SMPC to process encrypted data without exposing sensitive input.Ensure that all intermediate and output data is encrypted at rest and in transit.Deploy access policies that restrict inference to verified users and approved applications.Risk #3: model leakage of intellectual assetsModel leakage—or model extraction—happens when an attacker interacts with a deployed model in ways that allow them to reverse-engineer its structure, logic, or parameters. Once leaked, a model can be cloned, monetized, or used to bypass the very defenses it was meant to enforce. For businesses, this means losing competitive IP, compromising user privacy, or enabling downstream attacks.For developers and MLOps teams, the challenge is securing deployed models in a way that balances performance and privacy. If you're exposing inference APIs, you’re exposing potential entry points—but with the right controls and architecture, you can drastically reduce the risk of model theft.Mitigation and hardeningEnforce rate limits and usage quotas on all inference endpoints.Monitor for suspicious or repeated queries that indicate model extraction attempts.Implement model watermarking or fingerprinting to trace unauthorized model use.Obfuscate models before deployment using quantization, pruning, or graph rewriting.Disable or tightly control any model export functionality in your platform.Sign and verify inference requests and responses to ensure authenticity.Integrate security checks into CI/CD pipelines to detect risky configurations—such as public model endpoints, export-enabled containers, or missing inference authentication—before they reach production.Solution integration: native security integrationIntegrate model validation, packaging, and signing into CI/CD pipelines.Serve models from encrypted containers or TEEs, with minimal runtime exposure.Use container and image scanning tools to catch misconfigurations before deployment.Centralize monitoring and protection with tools like Gcore WAAP for real-time anomaly detection and automated response.How investing in AI security can save your business moneyFrom a financial point of view, the use of AI and machine learning in cybersecurity can lead to massive cost savings. Organizations that utilize AI and automation in cybersecurity have saved an average of $2.22 million per data breach compared to organizations that do not have these protections in place. This is because the necessity for manual oversight is reduced, lowering the total cost of ownership, and averting costly security breaches. The initial investment in advanced security technologies yields returns through decreased downtime, fewer false positives, and an enhanced overall security posture.Challenges aheadWhile securing the AI lifecycle is essential, it’s still difficult to balance robust security with a positive user experience. Rigid scrutiny can add additional latency or false positives that can stop operations, but AI-powered security can avoid such incidents.Another concern organizations must contend with is how to maintain current AI models. With threats changing so rapidly, today's newest model could easily become outdated by tomorrow’s. Solutions must have an ongoing learning ability so that security detection parameters can be revised.Operational maturity is also a concern, especially for companies that operate in multiple geographies. Well-thought-out strategies and sound governance processes must accompany the integration of complex AI/ML tools with existing infrastructure, but automation still offers the most benefits by reducing the overhead on security teams and helping ensure consistent deployment of security policies.Get ahead of AI security with GcoreAI workloads introduce new and often overlooked security risks that can compromise data integrity, model behavior, and intellectual property. By implementing practices like zero-trust architecture, encrypted inference, and native security integration, developers can build more resilient and trustworthy AI systems. As threats evolve, staying ahead means embedding security at every phase of the AI lifecycle.Gcore helps teams apply these principles at scale, offering native support for zero-trust AI, encrypted inference, and intelligent API protection. As an experienced AI and security solutions provider, our DDoS Protection and AI-enabled WAAP solutions integrate natively with Everywhere Inference and GPU Cloud across 210+ global points of presence. That means low latency, high performance, and proven, robust security, no matter where your customers are located.Talk with our AI security experts and secure your workloads today
Flexible DDoS mitigation with BGP Flowspec
For customers who understand their own network traffic patterns, rigid DDoS protection can be more of a limitation than a safeguard. That’s why Gcore supports BGP Flowspec: a flexible, standards-based method for defining granular filters that block or rate-limit malicious traffic in real time…before it reaches your infrastructure.In this article, we’ll walk through:What Flowspec is and how it worksThe specific filters and actions Gcore supportsCommon use cases, with example rule definitionsHow to activate and monitor Flowspec in your environmentWhat is the BGP Flowspec?BGP Flowspec (RFC 8955) extends Border Gateway Protocol to distribute traffic filtering rules alongside routing updates. Instead of static ACLs or reactive blackholing, Flowspec enables near-instantaneous propagation of mitigation rules across networks.BGP tells routers how to reach IP prefixes across the internet. With Flowspec, those same BGP announcements can now carry rules, not just routes. Each rule describes a pattern of traffic (e.g., TCP SYN packets >1000 bytes from a specific subnet) and what action to take (drop, rate-limit, mark, or redirect).What are the benefits of the BGP Flowspec?Most traditional DDoS protection services react to threats after they start, whether by blackholing traffic to a target IP, redirecting flows to a scrubbing center, or applying rigid, static filters. These approaches can block legitimate traffic, introduce latency, or be too slow to respond to fast-evolving attacks.Flowspec offers a more flexible alternative.Proactive mitigation: Instead of waiting for attacks, you can define known-bad traffic patterns ahead of time and block them instantly. Flowspec lets experienced operators prevent incidents before they start.Granular filtering: You’re not limited to blocking by IP or port. With Flowspec, you can match on packet size, TCP flags, ICMP codes, and more, enabling fine-tuned control that traditional ACLs or RTBH don’t support.Edge offloading: Filtering happens directly on Gcore’s routers, offloading your infrastructure and avoiding scrubbing latency.Real-time updates: Changes to rules are distributed across the network via BGP and take effect immediately, faster than manual intervention or standard blackholing.You still have the option to block traffic during an active attack, but with Flowspec, you gain the flexibility to protect services with minimal disruption and greater precision than conventional tools allow.Which parts of the Flowspec does Gcore implement?Gcore supports twelve filter types and four actions of the Flowspec.Supported filter typesGcore supports all 12 standard Flowspec match components.Filter FieldDescriptionDestination prefixTarget subnet (usually your service or app)Source prefixSource of traffic (e.g., attacker IP range)IP protocolTCP, UDP, ICMP, etc.Port / Source portMatch specific client or server portsDestination portMatch destination-side service portsICMP type/codeFilter echo requests, errors, etc.TCP flagsFilter packets by SYN, ACK, RST, FIN, combinationsPacket lengthFilter based on payload sizeDSCPQuality of service code pointFragmentMatch on packet fragmentation characteristicsSupported actionsGcore DDoS Protection supports the following Flowspec actions, which can be triggered when traffic matches a specific filter:ActionDescriptionTraffic-rate (0x8006)Throttle/rate limit traffic by byte-per-second rateredirectRedirect traffic to alternate location (e.g., scrubbing)traffic-markingApply DSCP marks for downstream classificationno-action (drop)Drop packets (rate-limit 0)Rule orderingRFC 5575 defines the implicit order of Flowspec rules. The crucial point is that more specific announcements take preference, not the order in which the rules are propagated.Gcore also respects Flowspec rule ordering per RFC 5575. More specific filters override broader ones. Future support for Flowspec v2 (with explicit ordering) is under consideration, pending vendor adoption.Blackholing and extended blackholing (eBH)Remote-triggered blackhole (RTBH) is a standardized protection method that the client manages via BGP by analyzing traffic, identifying the direction of the attack (i.e., the destination IP address). This method protects against volumetric attacks.Customers using Gcore IP Transit can trigger immediate blackholing for attacked prefixes via BGP, using the well-known blackhole community tag 65000:666. All traffic to that destination IP is dropped at Gcore’s edge.The list of supported BGP communities is available here.BGP extended blackholeExtended blackhole (eBH) allows for more granular blackholing that does not affect legitimate traffic. For customers unable to implement Flowspec directly, Gcore supports eBH. You announce target prefixes with pre-agreed BGP communities, and Gcore translates them into Flowspec mitigations.To configure this option, contact our NOC at noc@gcore.lu.Monitoring and limitationsGcore can support several logging transports, including mail and Slack.If the number of Flowspec prefixes exceeds the configured limit, Gcore DDoS Protection stops accepting new announcements, but BGP sessions and existing prefixes will stay active. Gcore will receive a notification that you reached the limit.How to activateActivation takes just two steps:Define rules on your edge router using Flowspec NLRI formatAnnounce rules via BGP to Gcore’s intermediate control planeThen, Gcore validates and propagates the filters to border routers. Filters are installed on edge devices and take effect immediately.If attack patterns are unknown, you’ll first need to detect anomalies using your existing monitoring stack, then define the appropriate Flowspec rules.Need help activating Flowspec? Get in touch via our 24/7 support channels and our experts will be glad to assist.Set up GRE and benefit from Flowspec today
Securing AI from the ground up: defense across the lifecycle
As more AI workloads shift to the edge for lower latency and localized processing, the attack surface expands. Defending a data center is old news. Now, you’re securing distributed training pipelines, mobile inference APIs, and storage environments that may operate independently of centralized infrastructure, especially in edge or federated learning contexts. Every stage introduces unique risks. Each one needs its own defenses.Let’s walk through the key security challenges across each phase of the AI lifecycle, and the hardening strategies that actually work.PhaseTop threatsHardening stepsTrainingData poisoning, leaksValidation, dataset integrity tracking, RBAC, adversarial trainingDevelopmentModel extraction, inversionRate limits, obfuscation, watermarking, penetration testingInferenceAdversarial inputs, spoofed accessInput filtering, endpoint auth, encryption, TEEsStorage and deploymentModel theft, tamperingEncrypted containers, signed builds, MFA, anomaly monitoringTraining: your model is only as good as its dataThe training phase sets the foundation. If the data going in is poisoned, biased, or tampered with, the model will learn all the wrong lessons and carry those flaws into production.Why it mattersData poisoning is subtle. You won’t see a red flag during training logs or a catastrophic failure at launch. These attacks don’t break training, they bend it.A poisoned model may appear functional, but behaves unpredictably, embeds logic triggers, or amplifies harmful bias. The impact is serious later in the AI workflow: compromised outputs, unexpected behavior, or regulatory non-compliance…not due to drift, but due to training-time manipulation.How to protect itValidate datasets with schema checks, label audits, and outlier detection.Version, sign, and hash all training data to verify integrity and trace changes.Apply RBAC and identity-aware proxies (like OPA or SPIFFE) to limit who can alter or inject data.Use adversarial training to improve model robustness against manipulated inputs.Development and testing: guard the logicOnce you’ve got a trained model, the next challenge is protecting the logic itself: what it knows and how it works. The goal here is to make attacks economically unfeasible.Why it mattersModels encode proprietary logic. When exposed via poorly secured APIs or unprotected inference endpoints, they’re vulnerable to:Model inversion: Extracting training dataExtraction: Reconstructing logicMembership inference: Revealing whether a datapoint was in trainingHow to protect itApply rate limits, logging, and anomaly detection to monitor usage patterns.Disable model export by default. Only enable with approval and logging.Use quantization, pruning, or graph obfuscation to reduce extractability.Explore output fingerprinting or watermarking to trace unauthorized use in high-value inference scenarios.Run white-box and black-box adversarial evaluations during testing.Integrate these security checks into your CI/CD pipeline as part of your MLOps workflow.Inference: real-time, real riskInference doesn’t get a free pass because it’s fast. Security needs to be just as real-time as the insights your AI delivers.Why it mattersAdversarial attacks exploit the way models generalize. A single pixel change or word swap can flip the classification.When inference powers fraud detection or autonomous systems, a small change can have a big impact.How to protect itSanitize input using JPEG compression, denoising, or frequency filtering.Train on adversarial examples to improve robustness.Enforce authentication and access control for all inference APIs—no open ports.Encrypt inference traffic with TLS. For added privacy, use trusted execution environments (TEEs).For highly sensitive cases, consider homomorphic encryption or SMPC—strong but compute-intensive solutions.Check out our free white paper on inference optimization.Storage and deployment: don’t let your model leakOnce your model’s trained and tested, you’ve still got to deploy and store it securely—often across multiple locations.Why it mattersUnsecured storage is a goldmine for attackers. With access to the model binary, they can reverse-engineer, clone, or rehost your IP.How to protect itStore models on encrypted volumes or within enclaves.Sign and verify builds before deployment.Enforce MFA, RBAC, and immutable logging on deployment pipelines.Monitor for anomalous access patterns—rate, volume, or source-based.Edge strategy: security that moves with your AIAs AI moves to the edge, centralized security breaks down. You need protection that operates as close to the data as your inference does.That’s why we at Gcore integrate protection into AI workflows from start to finish:WAAP and DDoS mitigation at edge nodes—not just centralized DCs.Encrypted transport (TLS 1.3) and in-node processing reduce exposure.Inline detection of API abuse and L7 attacks with auto-mitigation.180+ global PoPs to maintain consistency across regions.AI security is lifecycle securityNo single firewall, model tweak, or security plugin can secure AI workloads in isolation. You need defense in depth: layered, lifecycle-wide protections that work at the data layer, the API surface, and the edge.Ready to secure your AI stack from data to edge inference?Talk to our AI security experts
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