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

Abstract

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 research. As 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 increases. In addition, networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient. Thus, there is a need to augment them with intelligent network intrusion detection techniques. Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times. However, given the expected large network size, there is a necessity for extensive data processing for use in such anomaly detection methods. Deep learning solutions are lucrative options as they remove the necessity for feature selection. Therefore, 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 heightened. This 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 servers. Vehicular 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 paper. The proposed classifiers identify 18 different vehicular behavior types, the F1-scores ranging from 95.58% to 96.75%, much higher than the existing works. By running the classifiers on testbeds emulating edge servers, the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.