In-vehicular Networks Research Articles

Addressing Vulnerabilities in CAN-FD: An Exploration and Security Enhancement Approach

The rapid advancement of technology, alongside state-of-the-art techniques is at an all-time high. However, this unprecedented growth of technological prowess also brings forth potential threats, as oftentimes the security encompassing these technologies is imperfect. Particularly within the automobile industry, the recent strides in technology have brought about increased complexity. A notable flaw lies in the CAN-FD protocol, which lacks robust security measures, making it vulnerable to data theft, injection, replay, and flood data attacks. With the rising complexity of in-vehicular networks and the widespread adoption of CAN-FD, the imperative to safeguard the protocol has never been more crucial. This paper aims to provide a comprehensive review of the existing in-vehicle communication protocol, CAN-FD. It explores existing security approaches designed to fortify CAN-FD, demonstrating multiple multi-layer solutions that leverage modern techniques including Physical Unclonable Function (PUF), Elliptical Curve Cryptography (ECC), Ethereum Blockchain, and Smart contracts. The paper highlights existing multi-layer security measures that offer minimal overhead, optimal performance, and robust security. Moreover, it identifies areas where these security measures fall short and discusses ongoing research along with suggestions for implementing software and hardware-level modifications. These proposed changes aim to streamline complexity, reduce overhead while ensuring forward compatibility. In essence, the methods outlined in this study are poised to excel in real-world applications, offering robust protection for the evolving landscape of in-vehicular communication systems.

DeepSecDrive: An explainable deep learning framework for real-time detection of cyberattack in in-vehicle networks

Autonomous driving (AD) technologies are becoming increasingly popular, promising significant enhancements in road safety and efficiency. However, the susceptibility of autonomous vehicles to cyberattacks highlights the critical need for protecting the security and dependability of in-vehicular networks (IVNs). This study proposes a lightweight, efficient, and explainable deep learning framework, DeepSecDrive, for early detection of security threats against IVN. DeepSecDrive presents robust feature extractor units designed with deformable convolutions and a lightweight non-local network (LNLN). The former promotes dynamic change of receptive field to enhance extraction of complex features from the traffics of in-vehicle networks, while the latter enables learning contextual representations and non-local relationships in vehicular data. DeepSecDrive integrates Shapley Additive exPlanations to construct multi-level interpretability units, providing local and global explanations for model decisions in terms of feature contributions to the overall performance. Proof-of-concept experiments are conducted to evaluate the efficacy and durability of DeepSecDrive on real-world IVN attacks from Car-Hacking dataset, and the experimental results demonstrate the ability of our model to outperform state-of-the-art detection methods.

Reimagining Automotive Service-Oriented Communication: A Case Study on Programmable Data Planes

Automotive electrical and electronic (E/E) engineering architectures are being radically overhauled to satisfy the needs of the current automotive trends ranging from electrification to connected vehicles and autonomous driving. Novel networking technologies are being explored for future-proofing in-vehicular networks and making them ready for applications emerging from these trends. Programmable data plane technologies based on reconfigurable match-action tables have been successfully used in data center networking for handling data center-specific applications. In this article, we seek to explore programmable data planes for their utility in in-vehicular networks. To this end, we briefly discuss programmable data plane technologies and present a case study on implementing a distributed feedback control loop using SOME/IP, an automotive service-oriented communication middleware. We outline the parts of the SOME/IP stack that can be moved from the end systems to the network devices for achieving a better workload split using these technologies, thus improving system utilization.

Impact of Factors Influencing Cyber Threats on Autonomous Vehicles

ABSTRACT Advanced Technologies are transforming the Automotive industry and the pace of innovation is accelerating at a breakneck speed. Autonomous Vehicles (AVs) incorporate many different systems and technologies and their increased computer functionality and connectivity lead to enormous cybersecurity risk. The aim of this research is to explore the significant factors that influence cyber threats on AVs and to examine their level of importance. Partial Least Squares path modeling was preferred for research studies for its flexible modeling and identifying key drivers. The data analysis was carried out using ADANCO 2.0.Tables 1 to develop and evaluate the structural model and the causal relationships between the variables. Correlation of in-vehicular network vulnerabilities with trust and the correlation between the “workload of the driverless system” with cyber-attacks and cyber threats to AVs are two relations but have not been touched upon in previous studies. In this research, a modified framework is proposed based on the Cyber Cycle and integrated model of Diamond Model of Intrusion Analysis with the Active Cyber Defense Cycle.

Controller area network reliability: overview of design challenges and safety related perspectives of future transportation systems

The in-vehicular networked control system is among the most critical embedded processes. The controller area network (CAN) has prevailed intra-vehicle communication for decades. Meanwhile, requirements of future transportation systems are expected to emphasise the in-vehicle communication complexity, which endangers the reliability/safety of the intelligent navigation. At first, this study reviews the recent solutions proposed to overcome the CAN expanding complexity. Challenges that tomorrow's intelligent vehicles may raise for CAN reliability are investigated. The comprehensive coverage of current research efforts to remove the impact of these challenges is presented. Further, the in-vehicle system reliability of future automated vehicles is also related to the fault diagnosis performances. Hence, different classes of system-level diagnosis strategies are compared relatively to the requirements of automotive embedded networks. Furthermore, to thoroughly cover CAN reliability engineering issues, focus is given to the automotive validation techniques. The hardware in the loop, real-time analysis and computer-aided-design tools intervene in various phases along the in-vehicular network life cycle. Parameters that stand behind the efficiency and accuracy of these techniques in validating the new generation of vehicles are analysed. The authors finally draw some deductive predictions about the future directions related to the reliability of the intelligent transportation system in-vehicular communication.

Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.