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Anthropic-Cybersecurity-Skills/skills/detecting-beaconing-patterns-with-zeek/references/api-reference.md
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mukul975 27c6414ca5 Add folder anatomy (scripts/agent.py + references/api-reference.md) for 648 cybersecurity skills
Complete skill folder anatomy across all cybersecurity skills:
- scripts/agent.py: 80-150 line Python agents using real libraries (impacket,
  boto3, azure-mgmt-*, kubernetes, pefile, yara, scapy, shodan, stix2, etc.)
- references/api-reference.md: real API documentation with method signatures
- LICENSE: MIT license for all skill folders
2026-03-10 21:02:12 +01:00

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# API Reference: Detecting Beaconing Patterns with Zeek
## ZAT (Zeek Analysis Tools)
```python
from zat.log_to_dataframe import LogToDataFrame
from zat import zeek_log_reader
from zat.utils import dataframe_to_matrix
# Load conn.log into DataFrame
log_to_df = LogToDataFrame()
conn_df = log_to_df.create_dataframe('/path/to/conn.log')
# Select specific columns
conn_df = log_to_df.create_dataframe('conn.log',
usecols=['id.orig_h', 'id.resp_h', 'id.resp_p', 'ts', 'duration'])
# Read rows as dicts (streaming)
reader = zeek_log_reader.ZeekLogReader('conn.log')
for row in reader.readrows():
print(row)
# Tail mode (live monitoring)
reader = zeek_log_reader.ZeekLogReader('conn.log', tail=True)
for row in reader.readrows():
process(row)
# Convert to matrix for ML
to_matrix = dataframe_to_matrix.DataFrameToMatrix()
matrix = to_matrix.fit_transform(conn_df[features])
```
## Beaconing Detection Math
```python
import numpy as np
intervals = times.diff().dt.total_seconds().dropna().values
std_dev = np.std(intervals)
mean_val = np.mean(intervals)
cv = std_dev / mean_val # Coefficient of Variation
# cv < 0.3 = likely beacon (low jitter relative to interval)
```
## Key Zeek Log Fields
| Log | Key Fields |
|-----|-----------|
| conn.log | `id.orig_h`, `id.resp_h`, `id.resp_p`, `ts`, `duration`, `orig_bytes` |
| dns.log | `id.orig_h`, `query`, `qtype_name`, `answers`, `ts` |
| ssl.log | `id.orig_h`, `server_name`, `ja3`, `ja3s`, `ts` |
## Anomaly Detection with ZAT + scikit-learn
```python
from sklearn.ensemble import IsolationForest
odd_clf = IsolationForest(contamination=0.35)
odd_clf.fit(zeek_matrix)
anomalies = conn_df[odd_clf.predict(zeek_matrix) == -1]
```
### References
- ZAT: https://github.com/SuperCowPowers/zat
- ZAT examples: https://supercowpowers.github.io/zat/examples.html
- zat on PyPI: https://pypi.org/project/zat/