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How to Change SSH Port on Linux

  • By Gcore
  • September 6, 2023
  • 2 min read
How to Change SSH Port on Linux

In today’s digital age, securing server access is more crucial than ever. One effective method to thwart potential attackers is by altering the default SSH port. This article provides a comprehensive guide on how to safely make this change, ensuring not only a fortified security stance but also uninterrupted server access.

About the SSH Port

SSH (Secure Shell) is a protocol used primarily for secure remote server access and management. When we talk about the “SSH port,” we’re referring to the network port number on which an SSH server listens for incoming connections. By default, SSH servers listen on port 22. However, for security reasons, many system administrators choose to change this default port to another, less predictable number to help protect against automated attacks. Though it’s not mean that this is enough for comprehensive security, additional measures. 

Changing the SSH Port

Changing the default SSH port on a Linux system can add an additional layer of security against automated attacks. Here’s a step-by-step guide to help you make the change:

1. Backup the Configuration File. Before making any changes, it’s always a good practice to back up your SSH configuration file.

sudo cp /etc/ssh/sshd_config /etc/ssh/sshd_config.backup

2. Edit the SSH Configuration File. Open the SSHD configuration file with your preferred text editor. For this example, we’ll use nano.

sudo nano /etc/ssh/sshd_config

Sample Output:

3. Locate the Port Directive. Find the line that starts with Port. It should say Port 22 by default.

4. Change the Port Number. Edit the line to reflect your desired port number, preferably above 1024 to avoid conflicts with other standard services. For instance, to change it to port 2222, the line would look like:

Port 2222

5. Save and Close the File. If you’re using nano, press CTRL + O to write the changes, then press Enter, and CTRL + X to exit.

6. Adjust Firewall Rules. If you have a firewall enabled (like UFW or Firewalld), you’ll need to update its rules to allow connections on the new SSH port.

sudo ufw allow 2222/tcp

7. Restart the SSH Service. Apply the changes by restarting the SSH daemon.

sudo systemctl restart sshd

8. Test the New SSH Port. Before logging out of your current session, open a new terminal or SSH client and try connecting to the server using the new port to ensure everything works correctly:

ssh username@your_server_ip -p 2222

If you’re sure about your changes and the new port is working, and if you have a firewall enabled, consider closing the default port (22) to enhance security. Always be cautious when changing SSH settings, especially if you’re connecting remotely. Mistakes might lock you out of your server. Once you’re done, your Linux server will now accept SSH connections on the new port, enhancing its security against potential threats targeting the default port.

Conclusion

Looking to deploy Linux in the cloud? With Gcore Cloud, you can choose from Basic VM, Virtual Instances, or VPS/VDS suitable for Linux:

Choose an instance

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Cloud service providers typically guarantee 99.9% or higher uptime in service level agreements while maintaining enterprise-grade security standards.How much does cloud computing cost compared to on-premises infrastructure?Cloud computing typically costs 20-40% less than on-premises infrastructure due to shared resources, reduced hardware purchases, and lower maintenance expenses, according to IDC (2024). However, costs vary primarily based on usage patterns, with predictable workloads sometimes being cheaper on-premises while variable workloads benefit more from cloud's pay-as-you-go model.How do I choose between IaaS, PaaS, and SaaS?Choose based on your control needs. IaaS gives you full infrastructure control, PaaS handles infrastructure so you focus on development, and SaaS provides ready-to-use applications with no technical management required.

How to protect login pages with Gcore WAAP

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 use cases for geo-aware routing with Gcore DNS

If your audience is global but you’re serving everyone the same content from the same place, you're likely sacrificing performance and resilience. Gcore DNS (which includes a free-forever plan and enterprise-grade option) offers a straightforward way to change that with geo-aware routing, a feature that lets you return different DNS responses based on where users are coming from.This article breaks down how Gcore's geo-routing works, how to set it up using the GeoDNS preset in dynamic response mode, where it shines, and when you might be better off with a different option. We’ll walk through three hands-on use cases with real config examples, highlight TTL trade-offs, and call out what developers need to know about edge cases like resolver mismatch and caching delays.What is geo-aware DNS routing?Gcore DNS lets you return different IP addresses based on the user’s geographic location. This is configured using dynamic response rules with the GeoDNS preset, which lets you match on continent, country, region, ASN, or IP/CIDR. When a user makes a DNS request, Gcore uses the resolver’s location to decide which record to return.You can control traffic to achieve outcomes like:Directing European users to an EU-based CDN endpointSending users in regions with known service degradation to a fallback instanceBehind the scenes, this is done by setting up metadata pickers and specifying fallback behavior.For step-by-step guidance, see the official docs: Configure geo-balancing with Dynamic response.How to configure GeoDNS in Gcore DNSTo use geo-aware routing in Gcore DNS, you'll configure a dynamic response record set with the GeoDNS preset. This lets you return different IPs based on region, country, ASN, or IP/CIDR metadata.Basic stepsGo to DNS → Zones in the Gcore Customer Portal. (If you don’t have an account, you can sign up free and use Gcore DNS in just a few clicks.)Create or edit a record set (e.g., for app.example.com).Switch to Advanced mode.Enable Dynamic response.Choose the GeoDNS preset.Add responses per region or country.Define a fallback record for unmatched queries.For detailed step-by-step instructions, check out our docs.Once you’ve set this up, your config should look like the examples shown in the use cases below.Common use casesEach use case below includes a real-world scenario and a breakdown of how to configure it in Gcore DNS. These examples assume you're working in the DNS advanced mode zone editor with dynamic response enabled and the GeoDNS preset selected.The term “DNS setup” refers to the configuration you’d enter for a specific hostname in the Gcore DNS UI under advanced mode.1. Content localizationScenario: You're running example.com and want to serve language-optimized infrastructure for European and Asian users. This use case is often used to reduce TTFB, apply region-specific UX, or comply with local UX norms. If you're also localizing content (e.g., currency, language), make sure your app handles that via subdomains or headers in addition to routing.Objective:EU users → eu.example.comAsia users → asia.example.comAll others → global.example.comDNS setup:Host: www.example.comType: A TTL: 120 Dynamic response: Enabled Preset: GeoDNS Europe → 185.22.33.44 # EU-based web server Asia → 103.55.66.77 # Asia-based web server Fallback → 198.18.0.1 # Global web server2. Regional CDN failoverScenario: You’re using two CDN clusters: one in North America, one in Europe. If one cluster is unavailable, you want traffic rerouted regionally without impacting users elsewhere. To make this work reliably, you must enable DNS Healthchecks for each origin so that Gcore DNS can automatically detect outages and filter out unhealthy IPs from responses.Objective:North America → na.cdn.example.comEurope → eu.cdn.example.comEach region has its own fallbackDNS setup:Host: cdn.example.comType: A TTL: 60 Dynamic response: Enabled Preset: GeoDNS North America → 203.0.113.10 # NA CDN IP Backup (NA region only) → 185.22.33.44 # EU CDN as backup for NA Health check → Enabled for 203.0.113.10 with HTTP/TCP probe settingsEurope → 185.22.33.44 # EU CDN IP Backup (EU region only) → 203.0.113.10 # NA CDN as backup for EU Health check → Enabled for 185.22.33.44Note: Multi-level fallback by region isn’t supported inside one rule set—you need to separate them to keep routing decisions clean.3. Traffic steering for complianceScenario: You need to keep EU user data inside the EU for GDPR compliance while routing the rest of the world to lower-cost infrastructure elsewhere. This approach is useful for fintech, healthcare, or regulated SaaS workloads where regulatory compliance is a challenge.Objective:EU users → EU-only backendAll other users → Global backendDNS setup:Host: transactions.example.com Type: A TTL: 300 Dynamic response: Enabled Preset: GeoDNS Europe → 185.10.10.10 # EU regional API node Fallback → 198.51.100.42 # Global API nodeEdge casesGeoDNS works well, but it’s worth keeping in mind a few edge cases and limitations when you get set up.Resolver location ≠ user locationBy default, Gcore uses ECS (EDNS Client Subnet) for precise client subnet geo-balancing. If ECS isn’t present, resolver IP is used, which may skew location (e.g., public resolvers, mobile carriers). ECS usage can be disabled in the ManagedDNS UI if needed.Caching slows failoverEven if your upstream fails, users may have cached the original IP for minutes. Fallback + TTL tuning are key.No sub-regional precisionYou can route by continent, country, or ASN—but not city. City-level precision isn’t currently supported.Gcore delivers simple solutions to big problemsGeo-aware routing is one of those features that quietly solves big problems, especially when your app or CDN runs globally. With Gcore DNS, you don’t need complex infrastructure to start optimizing traffic flow.Geo-aware routing with Gcore DNS is a lightweight way to optimize performance, localize content, or handle regional failover. If you need greater precision, consider pairing GeoDNS with in-app geolocation logic or CDN edge logic. But for many routing use cases, DNS is the simplest and fastest way to go.Get free-forever Gcore DNS with just a few clicks

3 underestimated security risks of AI workloads and how to overcome them

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

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