What this article covers

• Understanding eBPF and why it's effective for Kubernetes runtime monitoring
• Types of runtime evidence you can collect (system calls, network activity, file operations, process execution)
• Deploying and configuring popular eBPF tools (Falco, Cilium/Hubble, Tetragon)
• Writing custom eBPF programs for specific monitoring needs
• Security considerations and performance optimization
• Real-world use cases with complete implementations
• Troubleshooting common eBPF deployment issues

Understanding eBPF for Kubernetes Monitoring

eBPF (extended Berkeley Packet Filter) runs sandboxed programs in the Linux kernel without modifying kernel source code or loading kernel modules. This provides kernel-level visibility into container behavior with minimal performance overhead.

Why eBPF for Kubernetes:

• Container-aware monitoring distinguishes between pods automatically
• No sidecar containers required (single agent per node)
• Access to events applications cannot hide (system calls, network, file I/O)
• Real-time detection and response capabilities
• Verified safe execution that cannot crash the kernel

Performance characteristics:

• Less than 1% CPU overhead for most workloads
• 100-300 MB memory per node
• In-kernel data aggregation reduces network transfer

Prerequisites

Before implementing eBPF monitoring, verify your environment meets these requirements:

Kubernetes requirements:

• Cluster admin access
• Ability to deploy DaemonSets
• Nodes must have /sys/kernel/btf/vmlinux (kernel 5.4+)

Types of Runtime Evidence You Can Collect

System Call Evidence

Captures low-level system interactions:

Valuable system calls to monitor:

• execve - Process execution• open, openat - File access

• connect, bind - Network operations

• ptrace - Debugging/injection attempts

• setuid, setgid - Privilege changes

Example evidence:

Network Evidence

Tracks connections, data transfer, DNS queries:

Network metrics to capture:

• Connection establishment and termination

• Data transfer volume

• DNS queries and responses

• Protocol anomalies

• Lateral movement patterns

File I/O Evidence

Monitors filesystem interactions:

File operations to track:

• Sensitive file access (credentials, keys, configuration)

• Binary modifications

• Unusual read/write patterns

• Configuration changes

Step 1: Deploy Falco for Security Monitoring

Falco provides runtime threat detection with pre-built security rules.

Install Falco via Helm

Create Custom Security Rules

Apply Custom Rules

Verify Falco is Running

Step 2: Deploy Cilium/Hubble for Network Visibility

Cilium provides network observability and policy enforcement using eBPF.

Install Cilium

Enable Hubble UI

Observe Network Flows

Step 3: Deploy Tetragon for Process Monitoring

Tetragon provides flexible security observability with custom tracing policies.

Install Tetragon

Create Tracing Policy

Apply Tracing Policy

Step 4: Store and Analyze Evidence

Set Up Elasticsearch for Event Storage

Configure Falco to Send Events

Query Stored Evidence

Step 5: Create Custom eBPF Program (Advanced)

For specialized monitoring needs, write custom eBPF programs.

Example: Monitor File Access

C eBPF Program:

Python Userspace Program:

Security Considerations

Required Privileges

eBPF tools require elevated privileges:

Capabilities needed:

• CAP_BPF (kernel 5.8+)
• CAP_SYS_ADMIN (older kernels)
• CAP_SYS_RESOURCE
• CAP_PERFMON (for performance monitoring)

Host access required:

• /sys filesystem
• /proc filesystem
• Kernel BTF
• Kernel modules directory

Minimize Privileges

Data Privacy

Sanitize sensitive data before storage:

RBAC for Evidence Access

Restrict who can view collected evidence:

Performance Optimization

Overhead Analysis

Optimization Techniques

1. Filter events in kernel (most efficient):

2. Aggregate metrics in kernel:

3. Use sampling for high-frequency events:

Configure sampling rate to reduce overhead:

4. Monitor tool performance:

Real-World Use Cases

Use Case 1: Detecting Cryptocurrency Mining

Falco rule:

Automated remediation:

Use Case 2: PCI-DSS Compliance Monitoring

Monitor access to cardholder data:

Generate compliance reports:

Use Case 3: Network Policy Validation

Verify network policies work correctly:

Automated validation:

Troubleshooting

Issue: eBPF Programs Won't Load

Symptoms:

Solutions:

Issue: Missing Events

Symptoms:Events are not captured or stored.Solutions:

Issue: High CPU Usage

Symptoms:eBPF tools consuming excessive CPU.Solutions:

Issue: Permission Denied

Symptoms:

Solutions:

Issue: Events Missing Kubernetes Metadata

Symptoms:Events lack pod name, namespace, or labels.Solutions:

Notes / Tips

Start Small

Phase 1 (Week 1-2): Deploy in monitoring-only mode

• Observe events without taking action
• Establish baselines for normal behavior

Phase 2 (Week 3-4): Enable alerts for critical events

• Alert on high-severity security events
• Build allow lists for known-good behavior

Phase 3 (Week 5+): Enable automated response

• Automated remediation for confirmed threats
• Continuous tuning based on false positives

Establish Baselines

Before enabling enforcement, capture normal behavior:

Layer Your Defenses

• Layer 1 (Network): Cilium for network flow monitoring
• Layer 2 (System Calls): Falco for process and file monitoring
• Layer 3 (Application): Custom eBPF for application-specific logic

Test Thoroughly

Monitor Tool Performance

eBPF tools should have minimal overhead:

• <1% CPU idle
• 2-5% CPU under active monitoring
• 100-300 MB memory per node

If overhead exceeds this, investigate event rates and consider sampling.

Related Articles

• [Kubernetes Security Best Practices](#)
• [Container Runtime Security](#)
• [Network Policy Enforcement](#)
• [Compliance Monitoring in Kubernetes](#)
• [Incident Response for Container Breaches](#)