Hybrid fuzzy multi-criteria decision making based multi cluster head dolphin swarm optimized IDS for VANET

Existing Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) based communication suffers from various security and performance issues, hence Cluster based Communication is preferred nowadays. However, Cluster based Communication adds extra overhead and burden on the Cluster Head (CH) in dense network scenarios which eventually introduces delay and hinders network performance. To reduce the overburdening of single CH, a multi cluster head scheme is proposed in which multiple nodes in a cluster can act as CH to share the load of single CH. For a selection of stable CH, Hybrid Fuzzy Multi-criteria Decision making approach (HF-MCDM) is proposed in which Fuzzy Analytic Hierarchy Process (AHP) and TOPSIS methods are clubbed together for optimal decision making. Further because of association of Vehicular Ad-hoc Network (VANET) with life-critical applications, there is a dire need for a security framework to detect various malevolent attacks. Machine Learning based Intrusion Detection System (IDS) like Support Vector Machine (SVM) is one of the approaches for curbing such attacks. These intrusion detection based mechanism can be combined with various existing optimization techniques to improve their performance, and Dolphin Swarm Algorithm is one such approach. Dolphins have many significant biological features like echolocation, exchange of information, coordination, and division of labor. These biological features combined with swarm intelligence can be utilized for optimizing the detection and accuracy of SVM based IDS. So in this paper, a Multi-Cluster Head anomaly based IDS optimized by Dolphin Swarm Algorithm has been proposed and its results are compared with various existing Security frameworks in terms of parameters like false positive, detection rate, detection time, etc. and it is observed that the proposed approach performs better.


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  • Accession Number: 01673724
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jun 26 2018 10:14AM