Estimating the Infection Spread Rate of Malicious Botnets Using Reconnaissance Worms in Botnet Defense System

In this paper, we propose a method for estimating the infection spread rate of malicious botnets in Botnet Defense Systems (BDS), a cybersecurity system designed to eliminate malicious botnets. The proposed method involves moving white-hat worms along pre-defined routes rather than diffusing them randomly. This approach is expected to eliminate observation biases. However, this approach prevents the BDS from obtaining observation results until the worm passes through the unobserved area. Therefore, the route of the worm was carefully designed so that BDS could obtain early observations. Our simulation result shows that the proposed method can estimate the infection spread rate with more than 90% accuracy. The proposed method enables strategy planning according to the value of the infection spread rate and can contribute to the effective elimination of malicious botnets by BDS.