---

name: detecting-beaconing-patterns-with-zeek description: > 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: cybersecurity subdomain: security-operations tags: [detecting, beaconing, patterns, with] version: "1.0" author: mahipal license: Apache-2.0

Detecting Beaconing Patterns with Zeek

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())