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
70 lines
2.2 KiB
Markdown
70 lines
2.2 KiB
Markdown
# 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
|
|
|
|
```python
|
|
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)
|
|
|
|
```python
|
|
# 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
|
|
|
|
```http
|
|
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
|
|
|
|
```python
|
|
# 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
|
|
|
|
```bash
|
|
python agent.py --file email_body.txt
|
|
python agent.py --file email_body.txt --baseline-file sender_style.json
|
|
```
|