Mathematical modeling of adaptive information security strategies using composite behavior models

Most existing adaptive information security approaches focus on simplified behavioral patterns and work as isolated models. This limits their effectiveness against advanced and dynamic cyber threats. Therefore, there is an emergent requirement for a mathematically unified framework that can dynamically capture and forecast the aggregate behavior of both the attacker and the defender in a complex environment. The paper proposes a mathematical modeling approach that combines composite behavior models into adaptive information security strategies. The framework encapsulates heterogeneous behavioral patterns into a unified dynamic model that can adapt to an ever-changing threat landscape. This result in novel adaptation rules derived from system dynamics and game theory, with the aim of enabling proactive defense mechanisms that can adapt to real-time challenges posed by adversary actors. The outcomes presented in this paper demonstrate strong improvements in threat detection, mitigation speed, and resource optimization through systematic model implementation, comprehensive simulation, and positive statistical hypothesis testing. The comparison reveals that the proposed method is generally superior to existing methods in scalability and effectiveness. It presents a new class of adaptive cybersecurity models that have deeper behavioral insights and enhanced resilience in complicated threat environments.