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
2.2 KiB
2.2 KiB
API Reference: Detecting BEC with AI
NLP Feature Extraction
| Feature | Description | BEC Signal |
|---|---|---|
| urgency_score | Ratio of urgency words to total | High = suspicious |
| pressure_score | Ratio of secrecy/pressure words | High = suspicious |
| financial_score | Ratio of financial terms | High = suspicious |
| authority_score | Ratio of executive title mentions | High = suspicious |
| caps_ratio | Uppercase character ratio | High = aggressive tone |
| unique_word_ratio | Vocabulary diversity metric | Low = template-like |
scikit-learn Classification Pipeline
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.ensemble import RandomForestClassifier
pipeline = Pipeline([
("tfidf", TfidfVectorizer(max_features=5000, ngram_range=(1, 2))),
("clf", RandomForestClassifier(n_estimators=100, random_state=42))
])
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)
Writing Style Analysis (Stylometry)
# Sentence length distribution for author verification
import re, math
sentences = re.split(r'[.!?]+', text)
lengths = [len(s.split()) for s in sentences if s.strip()]
mean_len = sum(lengths) / len(lengths)
variance = sum((l - mean_len)**2 for l in lengths) / len(lengths)
std_dev = math.sqrt(variance)
Microsoft Graph API - Suspicious Mail Rules
GET https://graph.microsoft.com/v1.0/users/{id}/mailFolders/inbox/messageRules
Authorization: Bearer {token}
# Detect forwarding rules (T1114.003)
GET https://graph.microsoft.com/v1.0/users/{id}/mailFolders/inbox/messageRules?$filter=actions/forwardTo ne null
Impersonation Signal Patterns
# Mobile signature (creates urgency excuse)
r"sent from my (iphone|ipad|android|mobile)"
# Discourages verification
r"(please|kindly).*(do not|don't).*(reply|respond|call)"
# Unavailability excuse
r"(i am|i'm).*(in a meeting|traveling|on a flight)"
# Time pressure
r"(handle|process|complete).*(today|immediately|by end of day)"
CLI Usage
python agent.py --file email_body.txt
python agent.py --file email_body.txt --baseline-file sender_style.json