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Intrusion Detection System for Energy Efficient Cluster Based Vehicular Adhoc Networks

R. Lavanya1,*, S. Kannan2

1 Department of Information Technology, E.G.S. Pillay Engineering College, Nagapattinam, 611002, India
2 Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, 611002, India

* Corresponding Author: R. Lavanya. Email:

Intelligent Automation & Soft Computing 2022, 32(1), 323-337.


A vehicular ad hoc network (VANET), a subfield of mobile adhoc network (MANET) is defined by its high mobility by demonstrating the dissimilar mobility patterns. So, VANET clustering techniques are needed with the consideration of the mobility parameters amongst the nearby nodes for constructing the stable clustering techniques. At the same time, security is also a major design issue in VANET, this can be resolved by the intrusion detection systems (IDS). 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 (FSVM), called FLC-OFSVM based on the Intrusion Detection System for VANET. The proposed FLC-OFSVM model involves two stages of operations namely clustering and intrusion detections. Primarily, FLC technique is employed for selecting an appropriate set of cluster heads (CHs) and for constructing the clusters. Besides, a lightweight anomaly IDS model named FSVM optimized with krill herd (KH) optimization algorithm is developed for detecting the existence of malevolent attacks in VANET. The KH algorithm based on the herding behavior of krills is used for optimally tuning the parameters of the FSVM model. In order to investigate the performance of the FLC-OFSVM model, an extensive set of simulations have been carried out and the results thus showcased that the OFSVM model has gained maximum outcome with an accuracy of 99.98%.


Cite This Article

R. Lavanya and S. Kannan, "Intrusion detection system for energy efficient cluster based vehicular adhoc networks," Intelligent Automation & Soft Computing, vol. 32, no.1, pp. 323–337, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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