Machine-Learning-Based White-Hat Worm Launcher Adaptable to Large-Scale IoT Network
Xiangnan Pan, Shingo Yamaguchi, Taku Kageyama · 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE) · 2021
This paper proposes a white-hat worm launcher based on machine learning (ML) adaptable to large-scale IoT network for Botnet Defense System (BDS). BDS is a kind of cyber-security systems that uses white-hat botnets to exterminate malicious botnets. White-hat bots defend an IoT system against malicious bots, but there is no discussion on the white-hat worms’ deployment in a large-scale IoT network. Therefore, We propose a divide and conquer algorithm, which allows us to apply the white-hat worm launcher to large-scale IoT networks. We introduced the proposed algorithm into the white-hat worm launcher. We modeled BDS and the launcher with agent-oriented Petri net PN2and confirmed the effect through the simulation of the PN2model.