Detecting beaconing patterns with zeek

Skill adriannoes/awesome-agentic-ai/cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/detecting-beaconing-patterns-with-zeek

329 agent skills (Cursor, Claude Code & Codex), 5,380 OpenClaw skills, 201 ML notebooks, 7 textbooks, 52 research papers, 17 industry reports for PMs, Designers & Developers.

Install

npx -y skills add adriannoes/awesome-agentic-ai --skill detecting-beaconing-patterns-with-zeek

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

'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.

The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.6 KB, as published. Nobody here has run it

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

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())