AI-Driven Evolutionary Honeypots for Polymorphic Cyber Threats

Polymorphic cyber threats continuously modify their code and behavioral patterns to circumvent traditional detection mechanisms, creating substantial challenges for conventional security frameworks. Honeypots, which function as decoy systems designed to attract attackers while logging their methodologies, provide a valuable defensive approach by capturing detailed attacker behaviors. This research introduces a proof-of-concept AI-driven evolutionary honeypot framework that combines transformer-based attack sequence prediction with reinforcement learning adaptation to combat polymorphic malware attacks. The evaluation utilized the Kaggle Polymorphic Malware Dataset 2025 across multiple threat categories. The transformer-based model achieved competitive performance with 81.68% accuracy, approaching traditional ensemble methods such as Random Forest (82.06%) while substantially outperforming deep learning baselines including BiLSTM (72.14%). The reinforcement learning adaptation component demonstrated practical feasibility with an 8% meaningful adaptation rate across 100 attack sequences, with Email Server configurations achieving 34.263 average engagement compared to 6.229 overall. Statistical significance testing confirmed large effect sizes compared to deep learning approaches (Cohen's D = 3.579 vs BiLSTM) while revealing that ensemble methods maintain slight advantages for this data type. The framework establishes the first integrated transformer + RL system for adaptive honeypot deployment, providing a foundation for future research in evolutionary cybersecurity defense. The research contributions include rigorous experimental methodology, comprehensive baseline comparisons, transparent performance assessment, and a complete Python implementation suitable for continued development.