An Efficiency-Accuracy Tradeoff for IDSs in VANETs with Markov-based Reputation Scheme

Vehicle Ad Hoc Networks (VANETs) are considered to be a next big thing that will remarkably change our lives, since this kind of technology is able to make our lives and roads safer. Intrusion Detection Systems (IDS) is an important technology that can mitigate both inner and outer threats for the vulnerable networks like VANETs. However, it is difficult to adopt the same IDSs that have been appropriately used in wired networks, due to the fast moving and highly dynamic nature of VANETs. Thus, in this paper, an Efficient IDS with a Markov-based Reputation Scheme is proposed, called EIDS-MRS. In EIDS-MRS, the Non-Linear Programming (NLP)-Optimization is designed as the efficient mechanism to reduce the execution time of IDSs in VANETs. Moreover, considering the security risks of NLP-Optimization, a Reputation Scheme based on the Hidden Generalized Mixture Transition Distribution (HgMTD) model, namely RS-HgMTD, is proposed for each vehicle in VANETs to evaluate the creditworthiness of their neighbors. Experiments show that the EIDS-MRS has better performance than other available IDSs in terms of detection rate, detection time and overhead.