mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
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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
1.8 KiB
1.8 KiB
API Reference: Detecting Beaconing Patterns with Zeek
ZAT (Zeek Analysis Tools)
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
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
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/