Attack Endgame: Proactive Security Approach for Predicting Attack Consequences in VANET

In the fast dynamic environment of Vehicle Ad Hoc Networks (VANETs), proactive security measures are necessary. Reactive security has been VAVNETs' guardian angel for some time, but now it is insufficient against current security attacks. Attack prediction is a promising solution capable of keeping up with the recent cyber security challenges. First, we need to understand where prediction fits in the attack process. To accomplish this, we introduce an attack life cycle in a VANET and exploit the proactive and retroactive phases. One of the proactive phases is the after-effect of the attack or what we call attack endgame. We use the Framework for Misbehavior Detection (F2MD) to simulate an attack effect with adverse side effects on road traffic. We implement traffic warning messages in F2MD. Then, we create attacks on these messages, namely "fake accident", and simulate the effect of these attacks on the vehicles while capturing the results using F2MD. We simulate the impact of acting on these messages or the attack endgame, which manifested in creating hazards. We use Recurrent Neural Network (RNN) models to predict the endgame of the fake accident attack on the road. We experiment with vanilla artificial neural network solutions to create a baseline. Afterward, we use Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) to build a stacked RNN model to predict the attack endgame at different time windows. They effectively predict the occurrence of a hazard up to 3.5 minutes ahead with over 80% accuracy.