LSTM-1DResNet: An Intrusion Detection Model for Connected and Autonomous Vehicles Based on Deep Learning

Connected and Autonomous Vehicles (CAVs) are pivotal for enabling intelligent mobility and autonomous driving, yet their inherent connectivity exposes critical vulnerabilities to cyber-attacks, which can precipitate life-threatening accidents, privacy breaches, and systemic failures in transportation networks. Given the escalating deployment of CAVs, ensuring robust cybersecurity is not merely a technical challenge but an urgent safety imperative. Traditional machine learning algorithms, such as Decision Trees (DT) and Support Vector Machines (SVM), often suffer from inadequate feature extraction in high-dimensional network traffic data, significantly compromising the accuracy of cyber-attack identification. Deep learning has emerged as a mainstream solution for Intrusion Detection Systems (IDS) due to its proficiency in handling complex data and automating feature extraction. To address these challenges, we propose LSTM-1DResNet, a novel deep learning-based intrusion detection model comprising an autoencoder and a classifier. The autoencoder innovatively integrates a Long Short-Term Memory Network (LSTM) with a one-dimensional convolutional residual module (Conv1D ResNet), substantially enhancing spatiotemporal feature extraction capabilities for high-dimensional traffic data. The classifier employs a Multilayer Perceptron (MLP) to deliver precise attack classification. The model was evaluated on both the NSL-KDD and CICIDS2017 datasets. On NSL-KDD, LSTM-1DResNet achieved 94.38% accuracy, outperforming standalone CNN and LSTM models by 11%. On CICIDS2017, it achieved 97.98% accuracy, precision of 98.11%, and recall of 97.98%, alongside high F1-scores(96.90%). This dual-dataset validation demonstrates the model's strong potential for enhancing intrusion detection in CAV-related contexts, particularly in addressing high-dimensional feature extraction challenges.