# Detecting Beaconing Patterns With Zeek

> 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.

- Skill: `autohandai-community-skills/detecting-beaconing-patterns-with-zeek` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add autohandai-community-skills/detecting-beaconing-patterns-with-zeek`
- Raw SKILL.md: https://api.skillmd.com/api/skills/autohandai-community-skills/detecting-beaconing-patterns-with-zeek/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- License: Apache-2.0
- Author: autohandai (https://skillmd.com/u/autohandai-community-skills)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/autohandai-community-skills/detecting-beaconing-patterns-with-zeek

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# 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.

```python
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

```python
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())
```