AI-Enabled Cyber Warfare Defense: A Unified Hybrid Framework for Intelligent Threat Detection

Abstract

The increasing sophistication of cyber threats poses serious challenges to national security (NS) and critical infrastructure (CI), requiring adaptive and intelligence-driven cyber defense mechanisms. While recent artificial intelligence (AI)-based methods have improved detection capabilities, many existing solutions focus on isolated threat categories or rely on single-layer detection models, limiting their robustness and deployment feasibility. This work presents a unified and adaptive artificial intelligence (AI)-enabled cyber threats detection framework that simultaneously addresses intrusion detection, malware detection and phishing detection within a cyber warfare context. The proposed framework integrates hybrid detection strategies with a threshold-based decision mechanism to balance detection effectiveness, false positive control and computational efficiency. A formal mathematical formulation supports feature representation, classification and evaluation. The framework is evaluated using multiple publicly available benchmark datasets under a consistent experimental setup. The experimental results demonstrate strong performance across threat categories, achieving detection accuracy above 96%, F1-scores exceeding 95% and false positive rates below 2%, highlighting the framework's effectiveness and deployment suitability for mission-critical cyber defense applications.