JoWUA

Abstract

This document presents the Cognitive-Generative Défense Framework (CGDF), which applies Artificial General Intelligence (AGI) principles to the next generation of cyber defense methodologies and anticipates, counters, and adjusts to Advanced Persistent Threats (APTs). This research, building from hands on Proof-0f-Concept (POC) evaluations of AI-integrated cyber defense tools in enterprise settings, combines operational functional knowledge with cognitive computational frameworks. In these evaluations, the effectiveness of behavioral analytics, anomaly detection, and adaptive learning models for threat identification and mitigation in dynamic, complex, and evolving threats was studied. It was noted that AI-embedded and rule-driven cyber defense architectures adequately situate problem diagnostic reasoning within the scope of adaptability situational frameworks. To counter the above, CGDF combines the Cognitive Threat Prediction Module with the Game-Theoretic Counteraction Engine (GTCE). Cognitive synthesis models, in real time and dynamic Aaron space. In contrast, GTCE generates mathematical models of defensive APTS as partially observable strategic games to calculate, optimally, dynamic counteraction measures. APT’ lifecycles simulated within and across APT networks showed CGDF reduced Adaptive Response Time (ART) and Detection Rate (DR) far beyond any remaining legacy AI/ML. This work aims to merge practical enterprise learnings with AGI-inspired cognition to achieve self-regulating, resilient cyber defence systems. This accomplishes the author’s vision towards the future of intelligent, autonomous, and reliable cybersecurity for pervasive computing ecosystems.