An intelligent IDS using bagging based fuzzy CNN for secured communication in vehicular networks

Internet of Vehicles consists of vehicular nodes that communicate with each other for making intelligent transportation systems, where cyber physical attacks are increasing continuously. Intrusion Detection System (IDS) is able to provide a better security solution for minimizing such cyber physical attacks. Many existing IDSs developed using classification algorithms fail to provide the expected intrusion detection accuracy and they exhibit higher false positive rates. Hence, an efficient Feature Selection Algorithm named Weightage and Ranking Based Feature Selection Algorithm and a Bagging based Fuzzy Convolutional Neural Network classification algorithm with Adam optimizer are proposed in this article which are used to identify the attacks more effectively using bagging with fuzzy inference in the deep convolutional neural network classifier. The proposed system was tested using benchmark and network trace datasets and proved that the proposed IDS enhances the detection accuracy and reduces the false positive rate.