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analyzing-network-flow-data-with-netflow

Analyze NetFlow v9 and IPFIX records, which summarize network connections, to spot traffic spikes, scanning, unusual outbound transfers, and periodic connections. Compare traffic against normal patterns and produce a prioritized findings report.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and periodic timing patterns.

설치 방법 보기

이럴 때 유용합니다

  • Investigating sudden increases in network traffic
  • Checking unusual outbound transfers for possible data theft
  • Finding periodic connections to external control servers
  • Preparing a prioritized report from network flow records

포함된 파일(4)

  • SKILL.md2.1 KB
  • LICENSE11.0 KB
  • references/api-reference.md1.6 KB
  • scripts/agent.py7.7 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Analyzing Network Flow Data with Netflow

When to Use

  • When investigating security incidents that require analyzing network flow data with netflow
  • When building detection rules or threat hunting queries for this domain
  • When SOC analysts need structured procedures for this analysis type
  • When validating security monitoring coverage for related attack techniques

Prerequisites

  • Familiarity with network security concepts and tools
  • Access to a test or lab environment for safe execution
  • Python 3.8+ with required dependencies installed
  • Appropriate authorization for any testing activities

Instructions

  1. Install dependencies: pip install netflow
  2. Collect NetFlow/IPFIX data from routers or use the built-in collector: python -m netflow.collector -p 9995
  3. Parse captured flow data using netflow.parse_packet().
  4. Analyze flows for:
    • Port scanning: single source to many destinations on same port
    • Data exfiltration: high byte-count outbound flows to unusual destinations
    • C2 beaconing: periodic connections with consistent intervals
    • Volumetric anomalies: traffic spikes beyond baseline thresholds
  5. Generate a prioritized findings report.
python scripts/agent.py --flow-file captured_flows.json --output netflow_report.json

Examples

Parse NetFlow v9 Packet

import netflow
data, _ = netflow.parse_packet(raw_bytes, templates={})
for flow in data.flows:
    print(flow.IPV4_SRC_ADDR, flow.IPV4_DST_ADDR, flow.IN_BYTES)

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