Self-Adapting Enterprise Threat Intelligence tool with Automated Deception and Real-Time Cyber Defense

Cyber threats have increased in sophistication and complexity, and traditional perimeter security defenses are failing to stop advanced persistent threats, zero-day attacks, and targeted intrusion attacks. The project aims to design, implement, and test an AI-powered Cyber Deception System. It is a proactive cybersecurity system that includes honeypot emulation, threat classification with machine learning, unsupervised anomaly detection and reinforcement learning based deception strategy adaptation in a single deployment that requires limited resources. The system will deploy 4 honeypot services: SSH, HTTP, FTP, Telnet. These services monitor the attacker's actions without revealing any production resources. Each source IP is given a behavioural fingerprint and passed on to a Random Forest classifier which classifies attack traffic into five categories: brute-force, reconnaissance, exploitation, lateral movement, and data exfiltration. It is accompanied by another anomaly detector called Isolation Forest for the detection of unknown attack patterns (zero-day and out-of-distribution) that are not present in the training data of the classifier. A composite threat score will add the confidence levels of the classifiers, the magnitude of the anomaly, how often it occurs in the behaviour and the recurrence signals for each attacker IP and can help prioritise responses according to the risk. A Q-Learning reinforcement learning agent selects a strategy from a catalogue of seven deception strategies. The agent learns the strategies that most help the attacker to spend more time during simulated engagement episodes. It shows consistent improvement in rewards as training continues.