Hybrid DNN-BiLSTM-aided intrusion detection and trust-clustering and routing-based intrusion prevention system in VANET
Prakash Sontakke, Nilkanth B. Chopade
Abstract
This paper focuses on developing an intrusion prevention and detection Vehicular Ad Hoc Network system by expert systems. The node data are composed of online sources. The gathered node data is fed to the extraction phase which can be done by using the autoencoder model. The extraction of features was given to the selection of feature stage to select the optimal features using the Beetle-Whale Swarm Optimization. The selected accurate features are employed in the intrusion detection stage with the help of a hybrid Deep Neural Network and Bidirectional Long Short Term Memory approach for the detection of network intrusion. The intrusion prevention takes place with the Trust-based routing protocol, where the malicious node is prevented by optimally selecting the routing path using the same B-WSO. The experimental analyses are performed to check the efficiency of the developed method by testing with conventional techniques.