mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 08:30:20 +00:00
24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
67 lines
1.8 KiB
Markdown
67 lines
1.8 KiB
Markdown
# API Reference: Detecting Beaconing Patterns with Zeek
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## ZAT (Zeek Analysis Tools)
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```python
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from zat.log_to_dataframe import LogToDataFrame
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from zat import zeek_log_reader
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from zat.utils import dataframe_to_matrix
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# Load conn.log into DataFrame
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log_to_df = LogToDataFrame()
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conn_df = log_to_df.create_dataframe('/path/to/conn.log')
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# Select specific columns
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conn_df = log_to_df.create_dataframe('conn.log',
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usecols=['id.orig_h', 'id.resp_h', 'id.resp_p', 'ts', 'duration'])
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# Read rows as dicts (streaming)
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reader = zeek_log_reader.ZeekLogReader('conn.log')
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for row in reader.readrows():
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print(row)
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# Tail mode (live monitoring)
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reader = zeek_log_reader.ZeekLogReader('conn.log', tail=True)
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for row in reader.readrows():
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process(row)
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# Convert to matrix for ML
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to_matrix = dataframe_to_matrix.DataFrameToMatrix()
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matrix = to_matrix.fit_transform(conn_df[features])
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```
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## Beaconing Detection Math
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```python
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import numpy as np
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intervals = times.diff().dt.total_seconds().dropna().values
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std_dev = np.std(intervals)
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mean_val = np.mean(intervals)
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cv = std_dev / mean_val # Coefficient of Variation
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# cv < 0.3 = likely beacon (low jitter relative to interval)
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```
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## Key Zeek Log Fields
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| Log | Key Fields |
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|-----|-----------|
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| conn.log | `id.orig_h`, `id.resp_h`, `id.resp_p`, `ts`, `duration`, `orig_bytes` |
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| dns.log | `id.orig_h`, `query`, `qtype_name`, `answers`, `ts` |
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| ssl.log | `id.orig_h`, `server_name`, `ja3`, `ja3s`, `ts` |
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## Anomaly Detection with ZAT + scikit-learn
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```python
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from sklearn.ensemble import IsolationForest
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odd_clf = IsolationForest(contamination=0.35)
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odd_clf.fit(zeek_matrix)
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anomalies = conn_df[odd_clf.predict(zeek_matrix) == -1]
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```
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### References
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- ZAT: https://github.com/SuperCowPowers/zat
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- ZAT examples: https://supercowpowers.github.io/zat/examples.html
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- zat on PyPI: https://pypi.org/project/zat/
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