The digital evolution of connected vehicles and the subsequent security risks emphasize the critical need for implementing in-vehicle cyber security measures such as intrusion detection and response systems. The continuous advancement of attack scenarios further highlights the need for adaptive detection mechanisms that can detect evolving, unknown, and complex threats. The effective use of ML-driven techniques can help address this challenge. However, constraints on implementing diverse attack scenarios on test vehicles due to safety, cost, and ethical considerations result in a scarcity of data representing attack scenarios. This limitation necessitates alternative efficient and effective methods for generating high-quality attack-representing data. This paper presents a context-aware attack data generator that generates attack inputs and corresponding in-vehicle network log, i.e., controller area network (CAN) log, representing various types of attack including denial of service (DoS), fuzzy, spoofing, suspension, and replay attacks. It utilizes parameterized attack models augmented with CAN message decoding and attack intensity adjustments to configure the attack scenarios with high similarity to real-world scenarios and promote variability. We evaluate the practicality of the generated attack-representing data within an intrusion detection system (IDS) case study, in which we develop and perform an empirical evaluation of two deep neural network IDS models using the generated data. In addition to the efficiency and scalability of the approach, the performance results of IDS models, high detection and classification capabilities, validate the consistency and effectiveness of the generated data as well. In this experience study, we also elaborate on the aspects influencing the fidelity of the data to real-world scenarios and provide insights into its application.

Increasing automation in vehicles enabled by increased connectivity to the outside world has exposed vulnerabilities in previously siloed automotive networks like controller area networks (CAN). Attributes of CAN such as broadcast-based communication among electronic control units (ECUs) that lowered deployment costs are now being exploited to carry out active injection attacks like denial of service (DoS), fuzzing, and spoofing attacks. Research literature has proposed multiple supervised machine learning models deployed as Intrusion detection systems (IDSs) to detect such malicious activity; however, these are largely limited to identifying previously known attack vectors. With the ever-increasing complexity of active injection attacks, detecting zero-day (novel) attacks in these networks in real-time (to prevent propagation) becomes a problem of particular interest. This paper presents an unsupervised-learning-based convolutional autoencoder architecture for detecting zero-day attacks, which is trained only on benign (attack-free) CAN messages. We quantise the model using Vitis-AI tools from AMD/Xilinx targeting a resource-constrained Zynq Ultrascale platform as our IDS-ECU system for integration. The proposed model successfully achieves equal or higher classification accuracy (> 99.5%) on unseen DoS, fuzzing, and spoofing attacks from a publicly available attack dataset when compared to the state-of-the-art unsupervised learning-based IDSs. Additionally, by cleverly overlapping IDS operation on a window of CAN messages with the reception, the model is able to meet line-rate detection (0.43 ms per window) of high-speed CAN, which when coupled with the low energy consumption per inference, makes this architecture ideally suited for detecting zero-day attacks on critical CAN networks.