Modeling Cyber Attacks and Resource Optimization for Enhancing Cyber Resilience

Abstract

As cyber threats evolve, traditional static defense systems struggle to adapt, often failing to address complex, multistage attacks. To bridge this gap, this paper proposes the Hybrid Intelligent Cyber Defense Framework (HICDF), a novel theoretical model that integrates Bayesian Threat Prediction, Markov Decision Processes (MDP), Deep Reinforcement Learning (DRL), Stackelberg Security Games (SSG), and Mixed Integer Linear Programming (MILP) into a unified, adaptive system. Unlike existing approaches, HICDF combines these methodologies into a real time adaptive loop, enabling continuous threat anticipation, dynamic response optimization, and efficient resource allocation. Key advantages of HICDF over traditional systems include improved adaptability to evolving threats, enhanced decision making under uncertainty, and optimized resource utilization. Designed for diverse environments such as cloud platforms, IoT ecosystems, and industrial control systems, HICDF demonstrates significant theoretical benefits. Future work will focus on empirical validation through real world simulations and practical implementations to confirm its effectiveness.