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

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