Deep learning-based multi-class misbehavior classification in fog-enabled vehicular ad hoc networks

Abstract

Traffic collisions and congestion represent significant challenges within intelligent transportation systems (ITS). Consequently, a vehicular ad-hoc network (VANET) has been established. Numerous architectures have been incorporated into VANETs to manage the extensive data generated by vehicles. Collaboration with fog computing is vital, particularly for applications requiring real-time processing. In addition, there is a growing demand for advanced intrusion detection methods. These techniques are employed to identify the optimal response that the fog server should provide based on data received from a vehicle. As the network continues to grow, the amount of data needing analysis also increases. Thus, deep learning approaches become increasingly efficient. The requirement for feature selection is reduced when leveraging deep learning techniques. This work uses two deep learning-based misbehavior classification schemes for intrusion detection in VANETs: Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). The vehicular data that the Roadside Units (RSUs) obtain is initially sent to the fog server for preprocessing and classification. This study suggests four classification methods, which include 4 LSTM, 2 LSTM-2 CNN, 3 CNN-1 LSTM, and 2 CNN-1 LSTM within two main classifiers. The first classifier uses them to classify each type of vehicle, while the second uses them to classify generic behavior categories. The results of the first classifier were higher than those of the existing work. The precision ranges from 94% to 98%. The recall ranges from 92% to 97%. The F1 scores range from 94% to 97%. The second classifier also scores the best results compared to other works. The precision, recall, and F1-score range from 99% to 99.66%. The time and space complexity are calculated for each model in each classifier.