An adaptive approach for cluster-based intrusion detection in VANET

Vehicular ad hoc network (VANET) is an external communication system for vehicles assisting intelligent transport. However, the identification of the intruders, who misuse the vehicles' genuine information, is a major concern. Hence, this paper introduces the adaptive elephant fuzzy system (AEFS) algorithm for detecting intrusion in the environment. The clustering of the vehicles in the environment is done using sparse fuzzy C-means (sparse FCM) clustering algorithm, which facilitates secure communication. The cluster head is provided with the AEFS that detects the intruder in VANET. The proposed algorithm is capable of detecting intruders, which enables the security of communication. The proposed AEFS algorithm obtained the maximal detection rate of 95.6829, minimal service response of 1.2059, minimal transmission overhead of 354.4174, and minimal verification delay in the absence of the attacks of 1.4593.