A DoS Attack Detection System Based on CNN and BiLSTM for Internet of Vehicles
Bin Wang, Ying Shang, Fang‐Ming Wang, Yingming Zeng · 2024
As the Internet of Vehicles (IoV) becomes established and continues to develop, its reliability and security assurance is becoming increasingly important. Denial of Service (DoS) attacks, characterized by ease of implementation by attackers and a variety of attack methods, have become one of the most serious security threats facing inter-vehicle and intra-vehicle networks. In response to the demand for DoS attack detection in IoV environments, we propose a deep learning-based intrusion detection model, which integrates 1D convolutional neural network (1D-CNN) and bi-directional long short-term memory (BiLSTM). On the basis of preprocessing techniques, including data cleaning and imbalanced data resampling, our proposed model is able to capture spatial features of network traffic using 1D-CNN and learn temporal features of traffic using BiLSTM. Experimental results demonstrate that the proposed model performs superior detection performance than the other six classic techniques on two public datasets, CICIDS2017 and Car-Hacking.