An iterative algorithm for determining contact-rate parameters in individual-based malware propagation models

Abstract This paper presents a novel iterative algorithm for determining contact-rate parameters in individual-based propagation models. Conventional mathematical epidemiological approaches frequently utilize compartmental models with uniform epidemiological coefficients, which are unable to account for the heterogeneity of individual behaviors and interactions. This research employs Physics Informed Neural Networks (PINNs), which have demonstrated efficacy in addressing differential equations and parameter estimation in both direct and inverse problems. PINNs are applied to individual-based models, which consider unique epidemiological characteristics for each individual or device, thus addressing the limitations of global models. The proposed method is particularly pertinent to the modeling of malware propagation in Internet of Things (IoT) networks, where each device exhibits distinct interaction patterns. The study employs a cellular automaton framework to simulate malware spread, thereby demonstrating the potential of PINNs in accurately estimating time-dependent contact rates. The methodology incorporates specific infection probabilities and recovery rates, thereby demonstrating its applicability to complex, heterogeneous systems. This approach represents a significant advancement in our understanding and prediction of the dynamics of disease and malware propagation, offering a robust tool for both biological and digital epidemiology. The findings indicate that individual-based models, enhanced with PINN-driven parameter estimation, can more accurately reflect real-world scenarios, thereby facilitating the development of more effective strategies for controlling epidemics and mitigating cybersecurity threats.