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mukul975
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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
70 lines
2.2 KiB
Markdown
70 lines
2.2 KiB
Markdown
# API Reference: Detecting BEC with AI
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## NLP Feature Extraction
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| Feature | Description | BEC Signal |
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|---------|-------------|------------|
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| urgency_score | Ratio of urgency words to total | High = suspicious |
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| pressure_score | Ratio of secrecy/pressure words | High = suspicious |
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| financial_score | Ratio of financial terms | High = suspicious |
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| authority_score | Ratio of executive title mentions | High = suspicious |
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| caps_ratio | Uppercase character ratio | High = aggressive tone |
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| unique_word_ratio | Vocabulary diversity metric | Low = template-like |
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## scikit-learn Classification Pipeline
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```python
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from sklearn.pipeline import Pipeline
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.ensemble import RandomForestClassifier
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pipeline = Pipeline([
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("tfidf", TfidfVectorizer(max_features=5000, ngram_range=(1, 2))),
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("clf", RandomForestClassifier(n_estimators=100, random_state=42))
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])
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pipeline.fit(X_train, y_train)
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predictions = pipeline.predict(X_test)
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```
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## Writing Style Analysis (Stylometry)
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```python
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# Sentence length distribution for author verification
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import re, math
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sentences = re.split(r'[.!?]+', text)
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lengths = [len(s.split()) for s in sentences if s.strip()]
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mean_len = sum(lengths) / len(lengths)
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variance = sum((l - mean_len)**2 for l in lengths) / len(lengths)
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std_dev = math.sqrt(variance)
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```
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## Microsoft Graph API - Suspicious Mail Rules
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```http
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GET https://graph.microsoft.com/v1.0/users/{id}/mailFolders/inbox/messageRules
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Authorization: Bearer {token}
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# Detect forwarding rules (T1114.003)
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GET https://graph.microsoft.com/v1.0/users/{id}/mailFolders/inbox/messageRules?$filter=actions/forwardTo ne null
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```
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## Impersonation Signal Patterns
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```python
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# Mobile signature (creates urgency excuse)
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r"sent from my (iphone|ipad|android|mobile)"
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# Discourages verification
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r"(please|kindly).*(do not|don't).*(reply|respond|call)"
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# Unavailability excuse
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r"(i am|i'm).*(in a meeting|traveling|on a flight)"
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# Time pressure
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r"(handle|process|complete).*(today|immediately|by end of day)"
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```
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## CLI Usage
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```bash
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python agent.py --file email_body.txt
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python agent.py --file email_body.txt --baseline-file sender_style.json
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```
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