The development of the complexity and connectivity of modern automobiles has caused a massive rise in the security risks of in-vehicle networks (IVNs). Nevertheless, existing IVN designs (e.g., controller area network) lack cybersecurity consideration. Intrusion detection, an effective method for defending against cyberattacks on IVNs while providing functional safety and real-time communication guarantees, aims to address this issue. Therefore, the necessity of its research has risen. In this paper, an IVN environment is introduced, and the constraints and characteristics of an intrusion detection system (IDS) design for IVNs are presented. A survey of the proposed IDS designs for the IVNs is conducted, and the corresponding drawbacks are highlighted. Various optimization objectives are considered and comprehensively compared. Lastly, the trend, open issues, and emerging research directions are described.

With the considerable growth of cybersecurity risks in modern automobiles, cybersecurity issues in the in-vehicle network environment have attracted significant attention from security researchers in recent years. Enhancing the cybersecurity ability of in-vehicle networks while considering the computing resource and cost constraints become an urgent issue. To address this problem, a novel information entropy-based method is proposed in this paper, which uses a fixed number of messages as sliding windows. By improving the sliding window strategy and optimizing the decision conditions, the detection accuracy is increased and the false positive rate is reduced. Experimental results demonstrate that the proposed method can provide real-time response to attacks with a considerably improved detection precision for intrusion detection in the in-vehicle network environment.

Energy consumption minimization is one of the primary design requirements for heterogeneous distributed systems. State-of-the-art algorithms are used to study the problem of minimizing the energy consumption of a real-time parallel application with precedence constrained tasks on a heterogeneous distributed system by introducing the concept of latest finish time (LFT) to reclaim the slack time based on the dynamic voltage and frequency scaling (DVFS) energy-efficient design optimization technique. However, the use of DVFS technique alone is insufficient, and the energy consumption reduction is limited because scaling down the frequency is restricted in practice. Furthermore, these studies merely minimize energy consumption through a local energy-efficient scheduling algorithm, such as reducing the energy consumption for each task on the fixed processor, rather than a global energy-efficient scheduling algorithm, such as reducing the energy consumption for each task on different processors. This study solves the problem of minimizing the energy consumption of a real-time parallel application on heterogeneous distributed systems by using the combined non-DVFS and global DVFS-enabled energy-efficient scheduling algorithms. The non-DVFS energy-efficient scheduling (NDES) algorithm is solved by introducing the concept of deadline slacks to reduce the energy consumption while satisfying the deadline constraint. The global DVFS-enabled energy-efficient scheduling (GDES) algorithm is presented by moving the tasks to the processor slacks that generate minimum dynamic energy consumptions. Results of the experiments show that the combined NDES&GDES algorithm can save up to 36.25-55.65 percent of energy compared with state-of-the-art counterparts under different scales, parallelism, and heterogeneity degrees of parallel applications.

With the rapid development of Internet of vehicles, connected vehicles, autonomous vehicles, and autonomous driving technologies, automotive Controller Area Networks (CAN) have suffered from numerous security threats. Deep learning models are the current mainstream intrusion detection techniques for threat analysis, and the state-of-the-art intrusion detection technique introduces the Generative Adversarial Networks (GAN) model to generate usable attacked samples to supplement the training samples, but it exists the limitations of rough CAN message block construction and fails to detect the data tampering threat. Based on the CAN communication matrix defined by the automotive Original Equipment Manufacturer (OEM) for a vehicle model, we propose an enhanced deep learning GAN model with elaborate CAN message blocks and the enhanced GAN discriminator. The elaborate CAN message blocks in the training samples can precisely reflect the real generated CAN message blocks in the detection phase. The GAN discriminator can detect whether each message has suffered from the data tampering threat. Experimental results illustrate that the enhanced deep learning GAN model has higher detection accuracy, recall, and F1 scores than the state-of-the-art deep learning GAN model under various attacks and threats.