Anomaly based intrusion detection system on semi-fog of Internet of Vehicle services by using deep learning approach

Abstract

The Internet of Vehicles (IoV) is a new paradigm of vehicular networks inspired by the adoption of the Internet of Things (IoT) in Vehicular Ad-hoc Networks (VANETs). There are mainly two types of applications used in IoV; safety, and non-safety applications. Because communication of IoV is vehicle-to-anything; it is compromised by different types of attacks, such as denial-of – service (DoS) attacks and impersonation attacks. DoS attacks create security problems in vehicles by flooding unnecessary messages and creating congestion that leads to the safety message being not delivered on time for the receiver vehicle. Attackers can get the advantage of releasing fake or malicious data to the vehicular network by controlling either onboard unit (OBU) or roadside unit (RSU). Previously published paper deploys their deep learning classifier module either of OBU, edge server, or RSU. As they employ on OBU of vehicular network attackers can penetrate anomalous data by controlling RSU. When they deploy on an edge server there is a communication delay and need a high cost for adding an edge server in different locations. Lastly, they deploy on trusted vehicle network infrastructure RSU, but they don’t use any alternative in the case of RSU stop working. By considering all the challenges of previous work, we are proposing anomaly based intrusion detection system on semi-fog of IoV by using deep learning algorithms. The deep learning algorithm is employed on RSU and OBU by selecting the vehicle as cluster head in case RSU stops working. In this work, we are using two deep neural network models, MLP (multilayer perceptron) and LSTM (long short term memory), for training and testing a dataset. In this deep learning, we train and test our deep neural network model by using WSN-DS and by customizing it to our deep learning scenario. The dataset consists of an imbalanced dataset distribution and we are applying SMOTE (synthetic minority oversampling technique) to balance the dataset. We are training and testing the dataset after the SMOTE technique is applied to it to remove overfitting problems. This paper is a novel one in terms of applying the deep learning classifier module on the fog layer or RSU and OBU to a selected vehicle by selecting a cluster head in case RSU stops working. After applying the SMOTE technique, we got 99.1%, 98.8%, 99.3%, and 99.6% accuracies for flooding, TDMA, grayhole and blackhole attack respectively. For validating our deep learning model we use FC-BOT-IOT dataset and we got 99.70% accuracy