The importance of vehicle security has increased in recent years in the automotive field, drawing the attention of both the industry and academia. This is due to the rise in cybersecurity threats caused by (1) the increase in vehicle connectivity schemes, such as the Internet of Things, vehicle-to-x communication, and over-the-air updates, and (2) the increased impact of such threats because of the added functionalities that are controlled by vehicle software. These causes and threats are further amplified in autonomous vehicles, which are generally equipped with more electronic control units (ECUs) that are connected through controller area networks (CANs). Due to the holistic nature of CANs, attacks on the networks can affect the functionality of all vehicle ECUs and the whole system. This can lead to a breach of privacy, denial of services, alteration of vehicle performance, and exposure to safety threats. Although cryptographic encryption and authentication algorithms and intrusion detection systems (IDS) are currently being used to detect and prevent CAN bus attacks, they have certain limitations. Therefore, this study proposed a mitigation scheme that can detect and prevent such attacks at the ECU level, which could address the limitations of existing algorithms. This study proposed the usage of a secure boot scheme to detect and prevent the execution of malicious codes, as the presence of one or more ECUs with a malicious code is the root cause of most CAN bus attacks. Secure boot schemes apply cryptographic data integrity algorithms to ensure that only authentic and untampered software can run on the vehicle’s ECUs. The selection of an appropriate cryptographic algorithm is important because it affects the secure boot schemes’ security level and performance. Therefore, this study also tested and compared the performance of the proposed secure boot scheme with five different data security algorithms implemented using the hardware security module (HSM) of the TC399 32-bit AURIX™ TriCore™ microcontroller through an electric autonomous vehicle’s control unit. The tests showed that the two most favorable schemes with the selected hardware are the secure boot scheme with the cipher-based message authentication code (CMAC), because it possesses the highest performance with an execution rate of 26.07 (ms/MB), and the secure boot scheme with the elliptic curve digital signature algorithm (ECDSA), because it provides a higher security level with an acceptable compromise in speed. This study also introduced and tested a novel variation of the ECDSA algorithm based on the CMAC algorithm, which was found to have a 19% performance gain over the standard ECDSA-based secure boot scheme.

In this paper, we propose a bus-independent hardware (HW)-based approach to secure long protocol data units (PDUs) in Automotive Open System Architecture (AUTOSAR)-based automotive electronic control units (ECUs). Our approach is based on extending previous works that implemented two AUTOSAR communication (COM) application-specific instruction set processors (ASIPs). COM ASIP V1 introduced two instructions to handle the transmission and reception of PDUs no larger than 8 bytes and signals no larger than 32 bits individually through send signal and receive signal instructions. COM ASIP V2 introduced two extra instructions to handle long signals and PDUs of arbitrary lengths. We extended the instruction set architecture (ISA) of our previous ASIPs by introducing six new instructions, in COM ASIP V3, to hash PDUs that contain these signals to authenticate transmission and reception of such PDUs. The experimental results show that COM ASIP V3 can handle (i.e., transmit, receive, calculate hash, or verify hash) a 64-byte controller area network flexible data-rate (CAN FD) frame in $1.575 \mathsf{\mu }\mathrm{s}$ and a 254-byte FlexRay frame in $6.301 \mathsf{\mu }\mathrm{s}$. These measurements indicate that the throughput of our new COM ASIP is much higher, 42× to 75×, than the throughput required by these communication buses.

Emotional distress during driving can greatly affect the safety and comfort of the driver. Being able to detect and react to the emotions of the driver would greatly improve in-car safety. It could also be utilized in a variety of different applications to improve the driving experience. In this paper, we introduce a brain signal-operated emotion recognition system that is specifically tailored for the Automotive Open System Architecture (AUTOSAR) framework. The proposed system acquires brain electroencephalography (EEG) signals of the driver, identifies the underlying emotion using machine learning techniques, and feeds that emotion into the car system where different car components can react to that input. Our results demonstrate the ability of the system to recognize two emotions, namely sadness versus happiness, from the recorded EEG with a mean accuracy of 89.7% across three examined subjects using subject-dependent data. Moreover, when training the system using data recorded from multiple subjects, a mean accuracy of 91.7% is achieved. Taken together, these results indicate the ability of the proposed approach to discriminate between sadness and happiness whose extreme expression could have a significant impact on driving behavior.

In this paper, we explore two pipeline techniques to enhance performance of communication operations in AUTomotive Open System Architecture (AUTOSAR)-based automotive electronic control units (ECUs), and securing these communication operations using an enhanced two-layer process based on the highly secure Galois/Counter Mode of Operation (GCM) algorithm. Our work is based on extending a previous work that implemented three versions of AUTOSAR communication (COM) application-specific instruction set processor (ASIP). We made two pipelined architectures on top of COM ASIP V3 (i.e. COM ASIP V4 and COM ASIP V5). These new versions of COM ASIP are able to handle all operations (i.e. transmitting signals/long signals, receiving signals/long signals, calculating hash, or verifying hash for protocol data units (PDUs) containing these signals using GCM) of our previously implemented COM ASIPs in a pipelined fashion. The experimental results show that COM ASIP V4 has a speedup of 2.25x to 2.39x over COM ASIP V3, and COM ASIP V5 has a speedup of 3.27x to 5.5x over COM ASIP V3. They also show that throughput of these new versions of COM ASIP is much more, 100x to 338x, than throughput required by Controller Area Network Flexible Data Rate (CAN FD) and FlexRay communication buses.

Human error is considered one of the major causes of car accidents. One potential approach to reduce human driving errors is to continuously monitor the driver's performance while driving. This could help in detecting potential risks and thus reduce the likelihood of accidents. In this paper, we introduce a machine learning system that analyzes the driver's brain activity to monitor and predict the driver's performance. While driving, the system monitors the driver's mental state by analyzing acquired Electroencephalography (EEG) signals. Additionally, the proposed system acquires EEG activity from the driver before driving and predicts the driving performance along the intended route. The proposed system is tailored for the Automotive Open System Architecture (AUTOSAR) framework. Our results demonstrate the ability of the system to classify the mental state of the driver in real-time into three states (focused, unfocused, and drowsy) with a mean accuracy of 96.5% across three examined subjects. The system also predicts the driver's performance before driving from the recorded EEG signals with a mean accuracy of 85%. These results indicate the utility of EEG signals analysis in enhancing the safety of futuristic automotive applications.

Recent advancements in Artificial Intelligence (AI) and the proliferation of the Internet of Things (loT) and mobile devices have generated enormous data volumes, necessitating advanced data processing closer to data sources through edge computing to enhance efficiency and reduce latency. Nonetheless, privacy concerns in cloud storage and data transmission, alongside the inadequacy of traditional privacy protections, have highlighted the vulnerability to sophisticated data breaches. This paper offers a survey on edge computing, emphasizing privacy and security challenges, evaluating threats, and exploring solutions to promote the development of secure, robust and privacy-preserving edge computing frameworks.