Epidemic Model of Algorithm-Enhanced Dedicated Virus through Networks

Abstract

Wi-Fi networks almost cover all active areas around us and, especially in some densely populated regions, Wi-Fi signals are strongly overlapped. The broad and overlapped coverage brings much convenience at the cost of great security risks. Conventionally, a worm virus can infect a router and then attack other routers within its signal coverage. Nowadays, artificial intelligence enables us to solve problems efficiently from available data via computer algorithm. In this paper, we endow the virus with some abilities and present a dedicated worm virus which can pick susceptible routers with kernel density estimation (KDE) algorithm as the attacking tasks automatically. This virus can also attack lower-encryption-level routers first and acquire fast-growing numbers of infected routers on the initial stage. We simulate an epidemic behavior in the collected spatial coordinate of routers in a typical area in Beijing City, where 56.0% routers are infected in 18 hours. This dramatical defeat benefits from the correct infection seed selection and a low-encryption-level priority. This work provides a framework for a computer-algorithm-enhanced virus exploration and gives some insights on offence and defence to both hackers and computer users.