Machine Learning White-Hat Worm Launcher for Tactical Response by Zoning in Botnet Defense System

Abstract

Malicious botnets such as Mirai are a major threat to IoT networks regarding cyber security. The Botnet Defense System (BDS) is a network security system based on the concept of “fight fire with fire”, and it uses white-hat botnets to fight against malicious botnets. However, the existing white-hat Worm Launcher of the BDS decides the number of white-hat worms, but it does not consider the white-hat worms’ placement. This paper proposes a novel machine learning (ML)-based white-hat Worm Launcher for tactical response by zoning in the BDS. The concept of zoning is introduced to grasp the malicious botnet spread with bias over the IoT network. This enables the Launcher to divide the network into zones and make tactical responses for each zone. Three tactics for tactical responses for each zone are also proposed. Then, the BDS with the Launcher is modeled by using agent-oriented Petri nets, and the effect of the proposed Launcher is evaluated. The result shows that the proposed Launcher can reduce the number of infected IoT devices by about 30%.