IDS and IPS for Kubernetes Intrusion Protection: Top 6 Solutions

Learn why you need IDS and IPS for Kubernetes (K8s) environments and discover how popular intrusion prevention and detection systems can safeguard your cluster.

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IDS and IPS for Kubernetes Intrusion Protection: Top 6 Solutions

The distributed nature of the Kubernetes containerized environment brings quite the challenges. About 42% of developers find security the top problem in container orchestration platforms, and 55% suffer release delays due to security issues. But effective solutions, namely IDS and IPS for Kubernetes, can improve your environment's visibility and help you prevent more threats.

In this article, we’ll explain the intrusion prevention (IPS) and intrusion detection systems (IDS) for Kubernetes (K8s). You’ll discover how they can help you detect suspicious activities, intercept attacks, and even optimize your performance. As a bonus, we’ll go through some of the most popular solutions on the market.

What are intrusion detection and intrusion prevention systems?

If you’re not particularly familiar with the topic, let us first go through the definitions of intrusion detection and prevention systems for Kubernetes.

Intrusion detection systems

An intrusion detection system is software that monitors your Kubernetes environment and generates alerts about anomalies. These could include malicious traffic patterns, unauthorized system calls, suspicious requests to Kubernetes API, DDoS attack patterns, or other vulnerability exploits.

intrusion detection system

IDS solutions analyze activities in your K8s cluster with the following methods:

  • Signature-based detection. The system detects possible incidents using signatures — patterns corresponding to already known malicious sequences. This method helps you discover already known attacks, but it’s limited against zero-day vulnerabilities (like unknown malware). Examples include your traffic going to unsecured domains, malicious byte sequences, and unusual requests with large packet sizes.
  • Anomaly detection. This method uses machine learning to build a trustful activity model and compare it to activities in your network. The system can alert you about login attempts from unusual locations, unidentified devices added without authorization, scan attacks (systematic scans of your network to determine opened ports), and zero-day exploits. Developing and training such a model is a challenge since it requires precise configurations and a lot of high-quality data.
  • Stateful protocol analysis. The system tracks your network security based on protocol standards issued by vendors. For example, it can notify you about inconsistencies in the authenticator used for sessions, suspicious activity for a specific group of users, and K8s arguments with unusual length or binary data. This method is insufficient by itself and can be inaccurate if your vendor doesn’t provide complete documentation about its protocols. 

Many IDS products use hybrid detection methods. In other words, they combine signature-based, anomaly detection, and stateful techniques to recognize more threats.

But you should know that IDS is a passive Kubernetes intrusion detection solution. These systems can send alerts to security information and event management (SIEM) systems and security teams for further assessment. And unlike IPS products, they don’t secure your endpoints or remediate vulnerabilities.

Intrusion prevention system

Intrusion prevention systems

Intrusion prevention systems work like IDS for Kubernetes, which monitors and flags potential threats, but additionally offer measures to protect your environment, like:

  • Stop an unusual behavior and block the traffic source in a container or an application
  • Quarantine traffic from inflicted network segments
  • Verify the integrity of damaged content and attempt to fix it
  • Check security controls for regulatory compliance violations
  • Optimize security configuration settings after intrusion attempts

You can consider IPS an augmentation of IDS. They can both detect suspicious behavior and traffic in your K8s clusters, pods, and applications. However, IPS can also stop an attack as it’s discovered.

This brings us to the next question.

Should you implement IDS and IPS for Kubernetes?

You might wonder, do you really need network intrusion prevention and detection systems for Kubernetes if you use a firewall or built-in tools from your provider? Here's why the answer is "yes."

The core principle of DevSecOps states that every system can be compromised given the attacker's skill, time, and motivation. Therefore, you need as many techniques to prevent as many attacks as possible and react to breaches faster if you want to minimize their impact.

Firewalls provide a foundation for security against some types of traffic, but many advanced attacks can slip through. Besides, they don’t protect your systems within the cluster if the attack breaches the "perimeter."

Kubernetes distribution isn’t failproof even on popular cloud computing platforms (like Amazon Web Services, Google Cloud, and Microsoft Azure). Although they provide robust access control, authentication and authorization mechanisms, this won’t be enough to cover all exploitable vulnerabilities.

You also need efficient means to monitor a large number of containers, pods, and the underlying code. That’s challenging enough, but the tracking is even more hectic if you deploy K8s across several cloud services.

So, what do you get by deploying IPS and IDS for Kubernetes? They let you monitor containers at runtime to detect more attacks and threats in time for you to patch up vulnerabilities. Moreover, an IPS can automatically quarantine malicious traffic and remediate the damage.

Now, enough with the theory. Let’s look at the solutions you can deploy for your Kubernetes infrastructure.

Top 6 intrusion detection and prevention products for K8s

Here are the most popular tools designed to protect your cluster and containerized applications from intrusions, zero-day exploits, and other anomalies.

intrusion detection

1. Aqua

The Aqua CNDR platform provides detection, prevention, and response automation tools across your entire K8s infrastructure. It uses behavioral detection and eBPF (Berkeley Packet Filter) to detect network attacks, evasion techniques, and unrecognized malware. 

This solution can be a great help to DevSecOps teams. Aqua CNDR assesses and scores your workloads to help your specialists identify vulnerable deployments, so you can prioritize mitigation efforts. It also lets you map out the inbound and outbound connections for each deployment, helping you identify likely entry points for attacks.

Aqua also identifies containers that didn’t come from your pipeline or were changed after deployment. Doing so helps you block containers and suspicious authorization attempts.

On the other hand, the platform lacks granularity in role-based access control (RBAC). So, you need to set permission for each cloud integration manually. It also doesn’t group alerts, which is troublesome for enterprises with a large number of microservices.

2. Datadog

Datadog monitors every node in your K8s, even if they’re distributed across several clouds. It detects attacks against infrastructure, monitors clusters for security misconfigurations, and automatically reports on CIS benchmarks. In addition to IDS, it tracks resource metrics (like CPU, memory, and traffic load) and logs autoscaling events. 

This software supports over 500 integrations with common monitoring and log management tools for real-time visibility into your infrastructure. You also get application performance monitoring (APM) and distributed tracing for transaction-level insight into your activities. All reports and logs are automatically tagged and grouped for your convenience. For instance, logs from your Redis containers will be tagged as service:redis and source:redis.

Sadly, Datadog lacks comprehensive documentation for its integrations and configurations. Even worse, its billing panel doesn't show how much you owe until the end of the month, which plays a dirty trick with your budget. Plus, you can’t set caps and limits, so there’s no way to know how much they'll charge you.

3. Falco 

Falco is an open-source cloud-native runtime security tool for threat detection across Kubernetes. You can run it directly in K8s or isolate it for extra protection in case of a breach. 

This system continuously monitors your cluster and cloud logs to detect unexpected behavior, configuration changes, or possible data leaks. It can also spot abnormal activity and intrusion based on Kubernetes audit logs, system calls, arguments, and properties of the calling process. 

It’s a trendy solution with many community-based frameworks, APIs, and customizable threat detection policies. You can also find various SDKs and documentation for plugin development.

Falco is an IDS solution without preventive features, meaning your security team has to analyze the alerts manually. Deployment at scale can be cumbersome without extra automation tools, especially if you have thousands of microservices. It also has limited alerting configuration options for the same container types.

4. Prisma Cloud

Prisma Cloud (previously known as Twistlock) hosts a variety of tools for real-time intrusion prevention of Kubernetes environments. This solution offers virtual firewalls that inspect your traffic for high-risk content, allowing only safe traffic to enter. Its management tools and RBAC also let you establish secure network boundaries across your clusters.

The workload identifier feature assigns every container with a cryptographic identity. This way, the system blocks network access if the communicating workload is not verified or unauthorized.

On top of that, Prisma Cloud offers compliance checks to prevent misconfigurations in the application lifecycle. It has over 400 customizable checks for common regulations (including GDPR, PCI DSS, and HIPAA) and numerous pre-built compliance templates. You can also generate comprehensive traffic, application, and threat detection reports for compliance audits.

You’ll have to regularly apply new updates to the platform, which can result in reconfigurations and maintenance overhead. The platform would also benefit from more detailed technical documentation and responsive support.

5. Tigera Calico

Tigera Calico mitigates data breaches and advanced persistent threats with a rule-based engine and machine learning algorithms. It evaluates all traffic flowing through your microservices and generates alerts after detecting unusual behavior. Additionally, the networking plugin is easily scalable with minimum overhead.

It’s possible to configure the alerts to trigger remediation measures. For instance, the platform can isolate rogue microservices from your network and apply new security policies to block similar threats in the future.

You can capture malicious traffic and trigger in-depth analysis for known signatures with Honeypods (fake pods in your K8s cluster). Additionally, the platform offers advanced encryption for extra protection of sensitive data. A handy DNS dashboard also helps you confirm and eliminate connectivity issues in your cluster.

However, the installation is complex, as you need to deploy numerous namespaces with dozens of pods to deploy the solution. Besides, the architecture consists of multiple sub-projects, meaning troubleshooting can be a bit tricky.

6. Wazuh

Wazuh is a free, open-source platform for threat prevention in containerized environments. It uses a signature-based approach and a ruleset to look for compromise indicators and detect security violations. The solution also lets you analyze the configurations of your endpoints to reduce the attack surface of your cluster.

This platform comes with various incident response solutions to address active threats. It provides a robust search engine and visualization tool to help security teams process flagged incidents. In addition, the platform features security controls for compliance with industry regulations.

As for the downsides, Wazuh has a clunky interface and poor documentation. Plus, despite being free, tools like CIS-CAT scanning need external licenses. Finally, you must configure monitoring capabilities and alerts manually.

We should point out that any IDS and IPS product requires you to configure network policies, signatures, and baseline behavior for your cluster. Otherwise, you risk overwhelming your team with false alerts, overlooking real threats, or hurting your productivity by blocking trustworthy traffic. 



Intrusion detection and prevention systems are adaptable safeguards for your containerized environment. IDS for Kubernetes analyzes traffic within specified parameters, logs activity records, and notifies you about emerging threats. Companies can improve network security even further with IPS, which automatically blocks malicious activities and remediates anomalies. 

But these products still require you to set up security policies and remediation rules to protect your Kubernetes without impacting performance. So it’s a good idea to find an experienced software development company to assist you. 

ALPACKED has a rich technical background in container orchestration platforms (including Kubernetes) and DevSecOps. We can help you select, set up, and manage IDS and IPS solutions. Just contact us to learn how to enhance the security of your K8s cluster.

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