Behavior analysis of self-evolving botnets

Machine learning techniques have been achieving significant performance improvements in various kinds of tasks, and they are getting applied in many research fields. While we benefit from such techniques in many ways, they can be a serious security threat to the Internet if malicious attackers become able to utilize them to detect software vulnerabilities. This paper introduces a new concept of self-evolving botnets, where computing resources of infected hosts are exploited to discover unknown vulnerabilities in non-infected hosts. We propose a stochastic epidemic model that incorporates such a feature of botnets, and show its behaviors through numerical experiments and simulations.