Hybrid Optimal Control for Malware Propagation in UAV-WSN System: A Stacking Ensemble Learning Control Algorithm

Abstract

Unmanned aerial vehicle (UAV) and wireless sensor network (WSN) system widely used in emergency sites monitoring, disaster relief and data collection, is vulnerable to malware attack. In this article, we establish a novel epidemic model to accurately describe the characteristics of malware cross-propagation and stochastic disturbances between UAV and WSN. To control their spread, the hybrid charging and patching strategies are optimized by the proposed stochastic hybrid optimal control (OC) method while minimizing control costs. An effective stacking ensemble learning control (SELC) approach is developed to obtain a sub-OC strategy. Extensive comparison and simulations are shown to validate the effectiveness of the proposed stochastic hybrid OC method and SELC method.