A Dynamic Immune Strategy for Blocking the Spreading of Worms in Vanets

Abstract

Currently VANETs still face many serious security issues. One of which is attacks from worms. To prevent the propagation of worms, different immune strategies have been proposed. One problem with these strategies is that they adopt a greedy strategy or random strategy to select immune nodes. These strategies do not consider the dynamic changes of the network topology caused by vehicle movement, which means that the strategies cannot effectively prevent a worm from spreading. In this paper, we propose a dynamic immune strategy. Considering the dynamic changes of VANETs, we use machine learning methods to predict vehicle positions at the next moment and combine the position information of vehicles at different times to evaluate the influence of a vehicle. We provide a method for computing the influence of vehicles. The vehicles with a large influence are selected as immune nodes. We compare the proposed immune strategy with several typical strategies, preemptive immunization, interactive immunization, blacklist isolation and degree immunization. The results show that the proposed method can prevent the spread of worms more effectively than existing techniques.