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detecting-beaconing-patterns-with-zeek

Examine Zeek connection logs for regular callbacks that may indicate communication with an attacker’s control server. Use ZAT and Pandas to group connections, measure timing variation, and identify source and destination pairs for further investigation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Performs statistical analysis of Zeek conn.log connection intervals to detect C2 beaconing patterns. Uses the ZAT library to load Zeek logs into Pandas DataFrames, calculates inter-arrival time standard deviation, and flags periodic connections with low jitter. Use when hunting for command-and-control callbacks in network data.

mukul975/Anthropic-Cybersecurity-Skills
설치 방법 보기

이럴 때 유용합니다

  • Hunting for periodic callbacks in Zeek connection logs
  • Comparing connection timing across source and destination pairs
  • Developing beaconing detection rules and hunting queries
  • Reviewing monitoring coverage for suspicious communications

포함된 파일(4)

  • SKILL.md2.6 KB
  • LICENSE11.0 KB
  • references/api-reference.md1.8 KB
  • scripts/agent.py6.3 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Detecting Beaconing Patterns with Zeek

When to Use

  • When investigating security incidents that require detecting beaconing patterns with zeek
  • 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 security operations 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

Load Zeek conn.log data using ZAT (Zeek Analysis Tools), group connections by source/destination pairs, and compute timing statistics to identify beaconing.

from zat.log_to_dataframe import LogToDataFrame
import numpy as np

log_to_df = LogToDataFrame()
conn_df = log_to_df.create_dataframe('/path/to/conn.log')

# Group by src/dst pair and calculate inter-arrival time
for (src, dst), group in conn_df.groupby(['id.orig_h', 'id.resp_h']):
    times = group['ts'].sort_values()
    intervals = times.diff().dt.total_seconds().dropna()
    if len(intervals) > 10:
        std_dev = np.std(intervals)
        mean_interval = np.mean(intervals)
        # Low std_dev relative to mean = likely beaconing

Key analysis steps:

  1. Parse Zeek conn.log into DataFrame with ZAT LogToDataFrame
  2. Group connections by source IP and destination IP pairs
  3. Calculate inter-arrival time intervals between consecutive connections
  4. Compute standard deviation and coefficient of variation
  5. Flag pairs with low coefficient of variation as potential beacons

Examples

from zat.log_to_dataframe import LogToDataFrame
log_to_df = LogToDataFrame()
df = log_to_df.create_dataframe('conn.log')
print(df[['id.orig_h', 'id.resp_h', 'ts', 'duration']].head())

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