Machine-Learning-Based White-Hat Worm Launcher in Botnet Defense System

Abstract

This article 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 cyber-security system that uses white-hat worms to exterminate malicious botnets. White-hat worms defend an IoT system against malicious bots, the BDS decides the number of white-hat worms, but there is no discussion on the white-hat worms' deployment in IoT network. Therefore, the authors propose a machine-learning-based launcher to launch the white-hat worms effectively along with a divide and conquer algorithm to deploy the launcher to large-scale IoT networks. Then the authors modeled BDS and the launcher with agent-oriented Petri net and confirmed the effect through the simulation of the PN2 model. The result showed that the proposed launcher can reduce the number of infected devices by about 30-40%.