<![CDATA[Computer viruses continue to be a serious problem on network infrastructure, with the ability to use AI to self-mutate and optimize its propagation. These virus variants that are influenced by AI are capable of adapting their attack method by themselves, which makes the known defense methods ineffective and necessitates advanced defense arrangements. The paper introduces a deterministic mathematical model with six compartments: Susceptible, Protected, Latent, Infected, Mutated, Recovered to study the macroscopic population dynamics of computer viruses based on the AI model. The basic reproduction number is calculated and a complete stability analysis of the equilibrium points is performed. An optimal control framework is developed to identify the best mitigation strategies with regard to proactive patching and reactive cleaning. An explicit existence theorem is proved and Pontryagin's Minimum Principle is used to describe the optimal control pair. To check the validity of the theoretical results, numerical simulations are carried out, which are based on the fourth-order Runge-Kutta method and the Forward-Backward Sweep Method. Our results show that AI-influenced viruses can be an endemic threat due to their increased mutation rate, but the dual-control strategy is a very reliable and cost-effective model for viral suppression. Sensitivity analyses, including 3D cost surfaces, also further validate this finding that synchronized proactive and reactive controls do an effective job of minimizing viral persistence and optimizing defense resource allocation.]]>
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