Intrusion Detection System for Energy Efficient Cluster based Vehicular Adhoc Networks

Abstract

A vehicular Adhoc Network, a subfield of Mobile Adhoc Network is defined by its high mobility and by demonstrating dissimilar mobility patterns. So, VANET clustering techniques are needed with the consideration of the mobility parameters amongst nearby nodes to construct stable clustering techniques. At the same time, security is also a major design issue in VANET which can be resolved by the Intrusion Detection Systems. In contrast to the conventional IDS, VANET based IDS are required to be designed in such a way that the functioning of the system does not affect the real-time efficiency of the performance of VANET applications. With this motivation, this paper presents an efficient Fuzzy Logic based Clustering with optimal Fuzzy Support Vector Machine, called FLC-OFSVM based Intrusion Detection System for VANET. The proposed FLC-OFSVM model involves two stages of operations namely clustering and intrusion detection. Primarily, FLC technique is employed to select an appropriate set of cluster heads and construct clusters. Besides, a lightweight anomaly IDS model named FSVM optimized with krill herd optimization algorithm is developed to detect the existence of malevolent attacks in VANET. The KH algorithm which is based on the herding behavior of krillsis used to optimally tune the parameters of the FSVM model. In order to investigate the performance of the FLC-OFSVM model, an extensive set of simulations are carried out and the results are investigated in terms of several performance measures.