A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

作     者：Tejasvi Alladi Varun Kohli Vinay Chamola F.Richard Yu Tejasvi Alladi;Varun Kohli;Vinay Chamola;F.Richard Yu

作者机构：Department of Computer Science and Information SystemsBITS-PilaniPilani Campus333031India Department of Electrical and Electronics Engineering&APPCAIRBITS-PilaniPilani Campus333031India Department of Systems and Computer EngineeringCarleton UniversityOttawaONK1S 5B6Canada 

出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)

年 卷 期：2023年第9卷第5期

页      面：1113-1122页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 081001[工学-通信与信息系统] 

基　　金：The work of Vinay Chamola and F.Richard Yu was supported in part by the SICI SICRG Grant through the Project Artificial Intelligence Enabled Security Provisioning and Vehicular Vision Innovations for Autonomous Vehicles and in part by the Government of Canada's National Crime Prevention Strategy and Natural Sciences and Engineering Research Council of Canada(NSERC)CREATE Program for Building Trust in Connected and Autonomous Vehicles(TrustCAV) 

主　　题：Vehicular Ad-hoc Networks(VANETs) Intelligent Transportation Systems(ITS) Artificial Intelligence(AI) Deep Learning Internet of Things(IoT) 

摘      要：With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of *** the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also *** addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be ***,there is a need to augment them with intelligent network intrusion detection *** machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent ***,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection *** learning solutions are lucrative options as they remove the necessity for feature ***,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more *** work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge *** data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this *** proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing *** running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of