AI-Based Deceptive Intelligence System for Proactive Cybersecurity

Abstract

Modern cyber threats have outpaced traditional security measures, demanding more intelligent and adaptive defense mechanisms. This research introduces an AI-driven deceptive intelligence framework that combines Generative Adversarial Networks (GANs) with Reinforcement Learning (RL) to create dynamic honeypots and automated threat response systems. The proposed system generates realistic, evolving decoy environments that mimic legitimate network services while continuously learning from attacker behaviors. Through real-time behavioral profiling and automated response mechanisms, the framework achieves 92.3% detection accuracy with only 3.87% false positives, significantly outperforming conventional intrusion detection systems like Snort and Suricata. The system extends average attacker engagement time to 3.5 minutes compared to 1.2 minutes in static honeypots, providing deeper intelligence gathering capabilities. With a mean response time of 1.8 seconds and modular container-based architecture, the solution offers seamless integration into hybrid network environments.

KEYWORDS: Artificial Intelligence (AI), Deceptive Intelligence, Generative Adversarial Networks (GANs), Reinforcement Learning (RL), Dynamic Honeypots, Cyber Threat Detection, Intrusion Detection Systems, Behavioral Profiling, Automated Threat Response, Network Security