# Detecting Beaconing Patterns With Zeek

> Performs statistical analysis of Zeek conn.log connection intervals to detect C2 beaconing patterns.

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.

- Page: https://ss33.ai/skills/mukul975/detecting-beaconing-patterns-with-zeek-36043667297
- Publisher: mukul975
- Category: [Security](https://ss33.ai/skills/category/security-w9g95g) › [Threat intel & forensics](https://ss33.ai/skills/category/security-w9g95g/threat-intel-and-forensics-rt4m7u)
- License: Apache-2.0
- Source: https://github.com/mukul975/anthropic-cybersecurity-skills/blob/54a798831d2266a3ca61ce68a7acb80b81160d57/skills/detecting-beaconing-patterns-with-zeek/SKILL.md

## Skill

SKILL.md: https://ss33.ai/api/v1/ss33/download/mukul975/detecting-beaconing-patterns-with-zeek-36043667297

# 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())
```