How to control the false positive rate of blocking malicious container processes?

To control the false positive rate of blocking malicious container processes, you need a combination of precise detection mechanisms, contextual analysis, and adaptive policies. Here’s how to approach it:

1. Behavioral Analysis Over Signature-Based Detection
   Relying solely on known malicious signatures (e.g., specific process names or hashes) leads to high false positives because legitimate processes might exhibit similar behaviors. Instead, use behavioral analysis to monitor process actions (e.g., unexpected network connections, privilege escalations, or file modifications).

   Example: A container process spawning a shell (/bin/sh) isn’t inherently malicious, but if it does so without a valid trigger (e.g., no user interaction or scheduled task), it’s suspicious. Behavioral analysis evaluates the context before flagging.

2. Baseline Normal Behavior
   Establish a baseline of "normal" process activities for your containers (e.g., typical CPU/memory usage, network endpoints, or file access patterns). Deviations beyond a statistically defined threshold are flagged for review rather than immediate blocking.

   Example: If a container normally reads from a specific database but suddenly starts scanning internal IP ranges, it’s worth investigating before blocking.

3. Contextual Rules and Whitelisting
   Define rules that incorporate context, such as:

   • Allowing specific processes only from trusted images (e.g., nginx in an official Docker image).
   • Whitelisting processes running under specific user IDs (e.g., non-root).
   • Excluding known false positives (e.g., a monitoring agent that legitimately accesses /proc).

   Example: A containerized CI/CD tool might legitimately execute git clone, but blocking it universally would break workflows. Whitelist the process for approved images.

4. Machine Learning (ML) for Anomaly Detection
   Use ML models trained on your environment’s historical data to distinguish between benign and malicious anomalies. These models improve over time by reducing false positives through feedback loops.

   Example: Tencent Cloud’s Tencent Cloud Container Security Service (TCSS) leverages ML to detect abnormal container behaviors while minimizing false alarms.

5. Gradual Enforcement (Monitor-Alert-Block)
   Start with a monitoring-only mode to log suspicious processes without blocking them. Analyze logs to refine rules before enforcing blocks.

   Example: Detect a process attempting to modify system libraries, log it, and verify if it’s a legitimate update or an attack before blocking similar actions cluster-wide.

6. Tencent Cloud Solutions

   • Tencent Cloud Host Security (HSM): Provides container process monitoring with customizable rules to reduce false positives.
   • Tencent Cloud TKE (Tencent Kubernetes Engine) + Security Add-on: Integrates behavioral analysis and runtime protection for containers, allowing fine-tuned policies.
   • Tencent Cloud Cloud Workload Protection (CWP): Uses threat intelligence and anomaly detection to block malicious processes while minimizing false triggers.

By combining these methods, you can dynamically adjust detection sensitivity to keep the false positive rate low while effectively blocking real threats.