Tokenization Representation and Deep-Learning-Based Intrusion Detection in Internet of Vehicles

The development of the Internet of Vehicles (IoV) has significantly enhanced connectivity and cooperation among road entities, leading to a more efficient, economical, and safer intelligent transportation system (ITS). However, this increased connectivity also exposes vehicles to a growing risk of cybersecurity threats through intravehicle and intervehicle networks. To secure IoV networks, many studies have focused on using intrusion detection systems (IDSs) based on deep learning methods to effectively detect cyber-attacks due to their ability to learn from large-scale data. Nonetheless, most existing IDSs rely on expert knowledge to manually design features, resulting in difficulties adapting to evolving attacks and information loss. To mitigate these limitations, this article presents a tokenization representation and attention mechanism-based convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) intrusion detection method. For feature extraction, we tokenize original traffic using natural language processing technique to represent discrete hexadecimal bytes as words, thus alleviating the need for manual feature design and allowing for the direct extraction of sequence patterns. For classification, we incorporate an attention mechanism into the CNN and BiLSTM architectures to enhance the accuracy of intrusion detection by focusing on critical information and capturing sequential patterns. The effectiveness of the proposed IDS is evaluated in both intravehicle and intervehicle network scenarios. Experimental results show that our method can detect various types of attacks with 100% accuracy on the Car-Hacking data set for the intravehicle network scenario. In the intervehicle network scenario utilizing the CICIoT2023 data set, our approach also achieves a high accuracy of 98%, outperforming existing methods.