Control strategy of complex pattern formations of malware nodes in Cyber-Physical Systems

In today’s digital age, malware attacks present substantial challenges. To address the security demands of cyber-physical systems (CPSs), this paper conducts a theoretical analysis of the complex Turing pattern dynamics in cross-diffusion-driven malware systems within CPSs. The study begins by linearizing the system to determine Turing instability conditions and the bifurcation parameter. Subsequently, through multi-scale analysis, it derives 2D amplitude equations near critical points and examines mode stability. The introduction of the state feedback control strategy mimics external interventions and bolsters network defenses. Numerical simulations demonstrate that malware nodes, when driven by cross-diffusion, generate a variety of Turing patterns. The proposed controller is capable of effectively suppressing or modifying these patterns. Implementing this control mechanism aids stable CPSs in undergoing Turing instability, thereby generating switchable node-distribution patterns.