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.
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Use detecting-beaconing-patterns-with-zeek to analyze [authorized Zeek conn.log] from [timeframe] in a test or lab environment, flag likely low-jitter C2 beaconing pairs, and report timing evidence and investigation priorities.
Required inputs
[authorized Zeek conn.log]
[timeframe]
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SKILL.md
name: detecting-beaconing-patterns-with-zeekdescription: '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. 'domain: cybersecuritysubdomain: security-operationstags: - network-security- zeek- c2-beaconing- conn-log-analysis- zat- threat-hunting- statistical-analysisversion: '1.0'author: mahipallicense: Apache-2.0nist_csf: - DE.CM-01- RS.MA-01- GV.OV-01- DE.AE-02mitre_attack: - T1071.001- T1071.004- T1573- T1008- T1095
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:
Parse Zeek conn.log into DataFrame with ZAT LogToDataFrame
Group connections by source IP and destination IP pairs
Calculate inter-arrival time intervals between consecutive connections
Compute standard deviation and coefficient of variation
Flag pairs with low coefficient of variation as potential beacons