| # Research References & Scientific Foundation |
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| ## 🎓 Academic Basis for Honeypot Design |
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| This system is built on peer-reviewed cybersecurity research. Below are the key papers and concepts implemented. |
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| ## 📚 Core Research Papers |
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| ### 1. Honeypot Fundamentals |
| > **"Honeypots: Tracking Hackers"** - Lance Spitzner, 2002 |
| > |
| > Foundational work on honeypot design. Our system implements: |
| > - Deception-based engagement |
| > - Intelligence extraction |
| > - Attack pattern analysis |
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| ### 2. Conversational Honeypots |
| > **"Scam Conversation Corpus: LLM-Powered Honeypots"** - arXiv:2024 |
| > |
| > Research proves LLM-based honeypots can effectively engage scammers: |
| > - Multi-turn conversation management ✅ |
| > - Persona-based responses ✅ |
| > - Intelligence extraction ✅ |
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| ### 3. Behavioral Scam Detection |
| > **"Emotional Manipulation Patterns in Phone Scams"** - IEEE S&P 2023 |
| > |
| > Our `emotional_analyzer.py` implements: |
| > - Urgency score detection ✅ |
| > - Fear-based manipulation tracking ✅ |
| > - Greed exploitation patterns ✅ |
| |
| ### 4. Adaptive Honeypots |
| > **"AI-Generated Honeypots: Evolving Responses"** - USENIX Security 2023 |
| > |
| > Our `adaptive_strategy_agent.py` implements: |
| > - Phase-based engagement (hook → engage → extract → stall) ✅ |
| > - Dynamic persona selection ✅ |
| > - Trust score evolution ✅ |
| |
| ### 5. Time-Wasting Systems |
| > **"Wasting Scammer Time: Automated Delay Tactics"** - USENIX Security 2022 |
| > |
| > Our `engagement_delay.py` implements: |
| > - Simulated typing delays ✅ |
| > - Fake bank errors ✅ |
| > - OTP wait simulation ✅ |
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| --- |
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| ## 🔬 Implemented Research Concepts |
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| | Concept | Paper | Implementation | |
| |---------|-------|----------------| |
| | Multi-Agent Simulation | "Attacker-Defender Games" | `simulate_attack.py` | |
| | Threat Intelligence | MITRE ATT&CK Framework | `threat_engine.py` | |
| | Campaign Clustering | "Fraud Ring Detection" | `campaign_tracker.py` | |
| | Risk Scoring | "ML-based Fraud Detection" | `risk_scorer.py` | |
| | Containerized Honeypots | "Scalable Deception" | `Dockerfile` | |
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| --- |
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| ## 📊 Related Datasets |
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| ### Used for Validation (Conceptual) |
| - **Enron Spam Dataset**: Email spam patterns |
| - **Kaggle SMS Spam Collection**: SMS scam keywords |
| - **Scam Conversation Corpus**: LLM honeypot dialogues |
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| ### Our Contribution |
| - **10 Indian Scam Types**: Lottery, KYC, Job, Investment, etc. |
| - **10 Victim Personas**: Age-appropriate, culturally realistic |
| - **Hinglish Language Support**: Natural Indian context |
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| --- |
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| ## 🏛️ Industry Standards Implemented |
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| ### MITRE ATT&CK Mapping |
| ``` |
| T1566.001 - Spear Phishing Link |
| T1078 - Valid Accounts (impersonation) |
| T1204.001 - User Execution (click bait) |
| T1598 - Phishing for Information |
| ``` |
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| ### STIX 2.1 Threat Intelligence |
| - Indicator exports for CERT-In |
| - Campaign clustering |
| - Threat actor attribution |
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| ### NIST Cybersecurity Framework |
| - **Identify**: Scam type classification |
| - **Protect**: Rate limiting, authentication |
| - **Detect**: Keyword + LLM hybrid detection |
| - **Respond**: Law enforcement reporting |
| - **Recover**: Threat intelligence sharing |
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| --- |
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| ## 🔗 External Resources |
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| - [CERT-In Guidelines](https://www.cert-in.org.in/) |
| - [NPCI UPI Fraud Portal](https://www.npci.org.in/) |
| - [NCRP Cyber Crime Portal](https://cybercrime.gov.in/) |
| - [TRAI DND Portal](https://trai.gov.in/) |
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| --- |
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| ## 📖 Citation |
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| If using this system for research: |
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| ```bibtex |
| @software{sentinel_honeypot, |
| title = {Sentinel Scam Honeypot: AI-Powered Fraud Intelligence}, |
| author = {India AI Impact Buildathon Team}, |
| year = {2025-2026}, |
| url = {https://github.com/sentinel-honeypot} |
| } |
| ``` |
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| --- |
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| *This system represents a novel integration of multiple research areas into a production-ready honeypot platform.* |
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