A filter model for intrusion detection system in Vehicle Ad Hoc Networks : a hidden Markov methodology - Liang, Junwei (2020)
Although Vehicle Ad Hoc Network (VANETs) as a new technology is being used in wide range of applications to improve the driving experience as well as safety, it is vulnerable to various type of network attacks. Literature studies have revealed several reliable approaches based on intrusion detection system (IDS), to protect VANETs against attacks. However, by those solutions, the overheads of IDSs are serious which cause too long detection time, especially when the number of vehicles increases. In this paper, we propose a novel filter model based hidden Markov model (HMM) (FM-HMM) for IDS to reduce the overhead and time for detection without impairing detection rate. To the best of our knowledge, this is the first work in the literature to model the state pattern of each vehicle in VANETs as a HMM to quickly filter the messages from the vehicles instead of detecting these messages. The FM-HMM consists of three modules, i.e., schedule, filter and update. In the schedule module, Baum–Welch algorithm is used to produce a HMM and its parameters for each neighbor vehicle. In the filter module, multiple HMMs are used with their parameters to forecast the future states of neighbor vehicles with which the messages from them are filtered. In the update module, a timeliness method is used to update HMMs and their parameters. Experiments show that the IDS with FM-HMM has a better performance in terms of detection rate, detection time and overhead.