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Update behavioral_analyzer.py
Browse files- behavioral_analyzer.py +43 -193
behavioral_analyzer.py
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"
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"
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"
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"
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"
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"nasty videos",
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],
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"payment_demand": [
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"bitcoin",
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"btc",
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"crypto",
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"wallet",
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"send money",
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"payment",
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],
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"time_pressure": [
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"48 hours",
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"24 hours",
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"countdown",
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"time is running out",
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"last chance",
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],
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},
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"phishing": {
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"credential_theft": [
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"verify your account",
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"login immediately",
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"confirm your password",
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"update your account",
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],
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"impersonation": [
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"security team",
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"support team",
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"admin",
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"it department",
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],
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"urgency": [
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"urgent",
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"immediately",
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"action required",
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"account suspended",
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],
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},
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"bec": {
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"financial_request": [
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"wire transfer",
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"bank details",
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"invoice attached",
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"payment request",
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],
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"authority_pressure": [
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"ceo",
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"director",
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"urgent request",
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"confidential",
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],
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}
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}
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# =========================
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# SCORING WEIGHTS
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# =========================
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BEHAVIOR_WEIGHTS = {
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"sextortion": {
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"device_compromise": 25,
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"surveillance": 20,
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"sexual_blackmail": 30,
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"payment_demand": 20,
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"time_pressure": 15,
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},
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"phishing": {
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"credential_theft": 30,
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"impersonation": 20,
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"urgency": 20,
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},
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"bec": {
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"financial_request": 35,
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"authority_pressure": 25,
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}
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}
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# =========================
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# MAIN ANALYZER
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# =========================
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def analyze_behavior(body: str) -> Dict:
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"""
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Analyze email body for behavioral attack patterns.
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Returns detailed signals, scores, and verdict.
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"""
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body_l = body.lower()
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findings = {}
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scores = {}
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matched_indicators = {}
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for attack_type, categories in BEHAVIOR_PATTERNS.items():
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findings[attack_type] = {}
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matched_indicators[attack_type] = []
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for category, patterns in categories.items():
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matched = any(p in body_l for p in patterns)
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findings[attack_type][category] = matched
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if matched:
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matched_indicators[attack_type].append(category)
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# Score calculation
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score = 0
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for category, matched in findings[attack_type].items():
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if matched:
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score += BEHAVIOR_WEIGHTS.get(attack_type, {}).get(category, 0)
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scores[attack_type] = min(score, 100)
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# Determine dominant attack
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dominant_attack = max(scores, key=scores.get)
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dominant_score = scores[dominant_attack]
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verdict = "Safe"
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if dominant_score >= 60:
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verdict = "Malicious"
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return {
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"
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"
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"signals": findings,
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"matched_indicators": matched_indicators,
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}
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return "No strong malicious behavioral patterns detected."
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attack = result["dominant_attack"]
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indicators = result["matched_indicators"].get(attack, [])
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return (
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f"Behavioral analysis strongly indicates **{attack.upper()}**.\n"
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f"Detected indicators: {', '.join(indicators)}.\n"
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f"Confidence Score: {result['confidence_score']}/100"
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)
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# =========================
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# STANDALONE TEST (OPTIONAL)
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# =========================
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if __name__ == "__main__":
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sample_text = """
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I have full access to your devices and recorded you via webcam.
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You must send bitcoin within 48 hours or the videos will be shared.
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"""
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result = analyze_behavior(sample_text)
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print(result)
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print(behavioral_summary(result))
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# behavioral_analyzer.py
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import re
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SEXTORTION_PATTERNS = [
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"i have access to all your devices",
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"i recorded you",
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"i installed a trojan",
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"i have your webcam",
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"masturbation",
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"adult websites",
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"send bitcoin",
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"btc",
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"wallet address",
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"48 hours",
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"countdown",
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"i will share the video",
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"i will expose",
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]
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def analyze_behavior(body: str):
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body_l = (body or "").lower()
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matched = [p for p in SEXTORTION_PATTERNS if p in body_l]
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if len(matched) >= 3:
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return {
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"dominant_attack": "sextortion",
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"confidence_score": 90, # 🔥 authoritative
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"verdict": "🚨 Malicious",
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"findings": matched,
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}
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return {
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"dominant_attack": "None",
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"confidence_score": 0,
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"verdict": "Unknown",
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"findings": [],
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}
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def behavioral_summary(result: dict) -> str:
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if result["dominant_attack"] == "sextortion":
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return (
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"Email exhibits sextortion behavior: claims of device compromise, "
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"recorded explicit content, cryptocurrency extortion, and urgency."
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)
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return "No strong behavioral threat detected."
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