A Guide to Building Real-Time Kernel-Level Monitoring of Network Policy Violation Traffic with eBPF

A Guide to Building Real-Time Monitoring of Network Policy Violation Traffic with eBPF (Prometheus/Grafana)

In DevOps and SRE, visibility is a matter of survival. As network environments grow more complex, simply knowing how much traffic flowed is no longer enough. You also need to verify policy compliance: Did this traffic follow our service security policies? Did communication between specific services take the intended path?

Traditional network monitoring tools mainly rely on aggregated higher-layer data such as SNMP or NetFlow/IPFIX. That approach has low overhead and is easy to analyze, but it easily misses the finest state changes as packets pass through the kernel, as well as detailed information about packets that were explicitly DROPped at the firewall or service mesh level.

This post walks through a concrete architecture and a step-by-step implementation that overcomes those limits: collecting network flow data directly at the kernel level and visualizing policy-violating traffic in real time with Prometheus and Grafana.

1. Why kernel-level eBPF?

eBPF (extended Berkeley Packet Filter) is a groundbreaking technology that lets you safely run user-defined code inside the Linux kernel. From a network monitoring perspective, using eBPF means attaching hooks directly into the OS packet-handling path (the kernel stack) so you can observe every point a packet passes through.

Conceptual flow of eBPF-based data collection

If the traditional approach is receiving a “results report,” eBPF is like having a live CCTV feed. Collection follows these five stages:

Packet generated $\rightarrow$ eBPF Hook (kernel level) $\rightarrow$ Custom Map write $\rightarrow$ Exporter conversion $\rightarrow$ Prometheus scrape

1. Hooking: Intercept packets at a specific network event point (e.g., XDP or TC hooks).
2. Processing: The eBPF program runs and analyzes the packet’s source/destination IP, port, and—most importantly—the policy decision (ACCEPT/DROP).
3. Mapping: Record the analyzed metadata (e.g., violation_count, allowed_flow_count) in a Map structure in kernel memory.
4. Exporting: A separate userspace agent (Exporter) accesses this kernel map and reads the data.
5. Scraping: Prometheus periodically collects metrics from the HTTP endpoint the Exporter exposes.

Implementing policy-violation logic (pseudocode)

The most critical piece is the logic that decides whether a policy was violated. This runs inside the eBPF program.

// eBPF 프로그램 내부 로직 (Pseudocode)
FUNCTION process_packet(packet):
    src_ip = extract_ip(packet.source)
    dst_ip = extract_ip(packet.destination)
    protocol = extract_protocol(packet)
    
    // 1. 정책 검사 로직 (예: 특정 IP 대역 접근 차단)
    IF is_blacklisted(src_ip) OR is_restricted_service(dst_ip, protocol):
        // 정책 위반 발생 시 카운터 증가
        atomic_increment_map("policy_violation_count", 1)
        // 패킷을 드롭 처리 (선택 사항)
        return DROP
    ELSE:
        // 정책 준수 트래픽
        atomic_increment_map("allowed_flow_count", 1)
        return PASS

2. Building the Prometheus stack: turning kernel data into metrics

Just because eBPF has written data into kernel maps does not mean Prometheus can read it. The custom Exporter bridges that gap.

The Exporter accesses the kernel map, reads the values, and exposes them in a format Prometheus understands (JSON/text via HTTP GET).

Prometheus scrape_config example

In a real environment, assume this Exporter serves metrics on a specific port (e.g., 9123). Add the following to the Prometheus config file (prometheus.yml):

scrape_configs:
  - job_name: 'ebpf_network_monitor'
    # Exporter가 실행되는 서버의 IP와 포트를 지정합니다.
    static_configs:
      - targets: ['localhost:9123'] 
    # 수집 간격은 정책 변화에 민감해야 하므로 짧게 설정합니다.
    scrape_interval: 15s

3. Implementing the Grafana dashboard: completing policy visibility

Once data is successfully collected in Prometheus, visualize it in Grafana. The goal is to make policy violations as conspicuous as possible.

Core dashboard components and PromQL queries

💡 Practitioner tip: When I first built this dashboard, I realized the key is not just looking at counters—it is using the rate() function to watch the rate of change. Catching the moment a violation counter spikes from 0 to 100 is a far more powerful anomaly-detection method than simply looking at cumulative violation counts.

4. Completing network observability and next steps

eBPF-based flow monitoring goes beyond simple traffic analysis and raises observability to the point of verifying business logic—policy compliance—at the infrastructure level.

The architecture is complete when you add alerting. Use Prometheus Alertmanager to build a workflow that immediately sends alerts to Slack or PagerDuty when a threshold such as rate(policy_violation_count[1m]) > 10 is exceeded.

With this deep kernel-level visibility, even if you introduce a service mesh you still gain the powerful advantage of tracing actual kernel behavior underneath it.

Frequently Asked Questions (FAQ)

Q1: Doesn’t eBPF impose too much system overhead? A1: One of eBPF’s biggest advantages is that it runs inside the kernel, so overhead is extremely low compared with copying or processing packets in userspace. With optimized hooks such as XDP (eXpress Data Path), it can operate at near-zero cost.

Q2: What knowledge do I need to build this monitoring system? A2: You need a basic understanding of the Linux kernel networking stack, Go (for writing the Exporter), eBPF C/Rust programming knowledge, and operational experience with Prometheus/Grafana. The learning curve is steep, but the value you get is equally high.

Q3: Can I use OpenTelemetry together with eBPF? A3: Yes. OpenTelemetry is a standardized framework for tracing and metrics collection. If you send kernel-level metrics collected by eBPF to an OpenTelemetry Collector via an Exporter, you can unify tracing and metrics data and build a more complete observability pipeline.