Overview
The AI Adaptive Cyber-Honeypot is an intelligent network deception platform designed to detect, capture, and analyze suspicious intrusion attempts. By simulating authentic service endpoints and analyzing captured payloads with offline LLM scripts, the system isolates attacker behavior and generates structured intrusion intelligence.
Problem & Approach
Static firewalls and passive signature matching struggle to detect zero-day probing patterns and automated credential-stuffing scanners before they identify vulnerabilities in production networks.
Engineered synthetic service listening layers in Python that capture connection attempts, log session commands into PostgreSQL, and run automated analysis to classify attack vectors.
System Architecture
- Capture Engine
- Python network socket listeners emulating vulnerable service interaction responses.
- Storage Layer
- PostgreSQL database storing raw session telemetry, IP headers, and attack traces.
- Analysis Pipeline
- Python threat classification scripts evaluating attacker payloads.
Features & Deliverables
- 01Synthetic Service EmulationImplemented
Simulates common network ports and services to attract and detain automated malicious scanners.
- 02Network Activity LoggingImplemented
Captures IP origin, timestamp, command payloads, and connection protocols into structured PostgreSQL tables.
- 03Offline LLM Threat ClassificationImplemented
Evaluates captured shell commands and payload sequences to classify threat severity and intent.
- 04Analytical Reporting DashboardImplemented
Filters and queries monitoring datasets to highlight repeated intrusion attempts and anomalous traffic.
Technical Challenges
ProblemEnsuring the honeypot capture environment remains isolated and cannot be leveraged for pivoting into internal hosts.
Resolution:Confined listening sockets to sandboxed network namespaces with zero outbound routing capabilities.
Contributions & Takeaways
My Contributions
- Architected the Python network listeners and socket handling logic.
- Designed the PostgreSQL telemetry schema and query mechanisms for intrusion analysis.
- Implemented offline LLM threat analysis scripts to classify attack patterns.
Key Learnings
- Deception technology provides invaluable early-warning telemetry on automated scanning tools.
- Structured database logging enables effective pattern recognition across distributed network probes.
Interested in discussing this project or working together?
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