# detecting-beaconing-patterns-with-zeek

> Analyzes Zeek conn.log connection intervals using statistical methods to detect C2 beaconing patterns, flagging periodic connections with low jitter.

- Skill: `mukul975/detecting-beaconing-patterns-with-zeek` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds add mukul975/detecting-beaconing-patterns-with-zeek`
- Raw SKILL.md: https://api.skillmd.com/api/skills/mukul975/detecting-beaconing-patterns-with-zeek/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security, Data & Analytics, Data Analysis, Incident Response
- Tags: Beaconing, C2, Conn Log, Network Traffic, Pandas, Threat Hunting, Zat, Zeek
- License: Apache-2.0
- Author: mukul975 (https://skillmd.com/u/mukul975)
- Updated: 2026-07-06
- Page: https://skillmd.com/skills/mukul975/detecting-beaconing-patterns-with-zeek

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# 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.

```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())
```