Vehicular ad hoc networks (VANETs) are becoming the most promising research topic in intelligent transportation systems, because they provide information to deliver comfort and safety to both drivers and passengers. However, unique characteristics of VANETs make security, privacy, and trust management challenging issues in VANETs' design. This survey article starts with the necessary background of VANETs, followed by a brief treatment of main security services, which have been well studied in other fields. We then focus on an in-depth review of anonymous authentication schemes implemented by five pseudonymity mechanisms. Because of the predictable dynamics of vehicles, anonymity is necessary but not sufficient to thwart tracking an attack that aims at the drivers' location profiles. Thus, several location privacy protection mechanisms based on pseudonymity are elaborated to further protect the vehicles' privacy and guarantee the quality of location-based services simultaneously. We also give a comprehensive analysis on various trust management models in VANETs. Finally, considering that current and near-future applications in VANETs are evaluated by simulation, we give a much-needed update on the latest mobility and network simulators as well as the integrated simulation platforms. In sum, this paper is carefully positioned to avoid overlap with existing surveys by filling the gaps and reporting the latest advances in VANETs while keeping it self-explained.

The public key infrastructure-based authentication protocol provides basic security services for the vehicular ad hoc networks (VANETs). However, trust and privacy are still open issues due to the unique characteristics of VANETs. It is crucial to prevent internal vehicles from broadcasting forged messages while simultaneously preserving the privacy of vehicles against the tracking attacks. In this paper, we propose a blockchain-based anonymous reputation system (BARS) to establish a privacy-preserving trust model for VANETs. The certificate and revocation transparency is implemented efficiently with the proofs of presence and absence based on the extended blockchain technology. The public keys are used as pseudonyms in communications without any information about real identities for conditional anonymity. In order to prevent the distribution of forged messages, a reputation evaluation algorithm is presented relying on both direct historical interactions and indirect opinions about vehicles. A set of experiments is conducted to evaluate BARS in terms of security, validity, and performance, and the results show that BARS is able to establish a trust model with transparency, conditional anonymity, efficiency, and robustness for VANETs.

The privacy-preserving authentication is considered as the first line of defense against the attacks in addition to preserving the identity privacy of the vehicles in the vehicular ad hoc networks (VANETs). However, the existing authentication schemes suffer from drawbacks such as nontransparency of the trusted authorities (TAs), heavy workload to revoke certificates, and high computation overhead to authenticate identities and messages. In this paper, we propose a blockchain-based privacy-preserving authentication (BPPA) scheme for VANETs. In BPPA, all the certificates and transactions are recorded permanently and immutably in the blockchain to make the activities of the semi-TAs transparent and verifiable. However, it remains a challenge how to use such blockchain effectively for authentication in real driving scenarios (e.g., high speed or large amount of messages during congestion). With a novel data structure named the Merkle Patricia tree (MPT), we extend the conventional blockchain structure to provide a distributed authentication scheme without the revocation list. To achieve conditional privacy, we allow a vehicle to use multiple certificates. The linkability between the certificates and real identity is encrypted and stored in the blockchain and can only be revealed in case of disputes. We evaluate the validity and performance of BPPA on the Hyperledger Fabric (HLF) platform for each entity. The experimental results show that the distributed authentication can be processed by individual vehicles within 1 ms, which meets the real-time requirement and is much more efficient, in terms of the processing time and storage requirement, than existing approaches.

The Internet of Things (IoT) is an industry-recognized next intelligent life solution that increases the level of comfort, efficiency, and automation for citizens through numerous sensors, smart devices, and cloud stations connected physically. As an important application scenario of IoT, the Internet of Vehicles (IoV) plays an extremely critical role in the intelligent transportation field. In fact, the In-Vehicle Network of smart vehicles that are recognized as the core roles in intelligent transportation is currently the Controller Area Network (CAN). However, the In-Vehicle CAN bus protocol has several vulnerabilities without any encryption, authentication, or integrity checking, which severely threatens the safety of drivers and passengers. Once malicious attackers hack the vehicular gateway and obtain the access right of the CAN, they may control the vehicle based on the vulnerabilities of the CAN bus protocol. Given the severe security risk of CAN, we proposed the CANsec, a practical In-Vehicle CAN security evaluation tool that simulates malicious attacks according to major attack models to evaluate the security risk of the In-Vehicle CAN. We also show a usage case of the CANsec without knowing any information from the vehicle manufacturer.

Deep neural network (DNN)-based transformer models have demonstrated remarkable performance in natural language processing (NLP) applications. Unfortunately, the unique scaled dot-product attention mechanism and intensive memory access pose a significant challenge during inference on power-constrained edge devices. One emerging solution to this challenge is computing-in-memory (CIM), which uses memory cells for logic computation to reduce data movement and overcome the memory wall. However, existing CIM designs do not support high-precision computations, such as floating-point operations, which are essential for NLP applications. Furthermore, CIM architectures require complex control modules and costly peripheral circuits to harness the full potential of in-memory computation. Hence, this article proposes a scalable RRAM-based in-memory floating-point computation architecture (RIME) that uses single-cycle NOR, NAND, and minority logic to implement in-memory floating-point operations. RIME features efficient parallel and pipeline capabilities with a centralized control module and a simplified peripheral circuit to eliminate data movement during computation. Furthermore, the article proposes pipelined implementations of matrix–matrix multiplication (MatMul) and softmax functions, enabling the construction of a transformer accelerator based on RIME. Extensive experimental results show that compared with GPU-based implementation, the RIME-based transformer accelerator improves timing efficiency by $2.3\times $ and energy efficiency by $1.7\times $ without compromising inference accuracy.

Modern vehicles are equipped with more than 100 Electrical Control Units (ECUs) with over 2500 signals to transmit internally. The application of advanced electronics and communication techniques helps a vehicle transform from an information island into a powerful distribution center. However, a large number of ECUs have introduced a wider range of security threats for vehicles. The attackers can compromise a vehicle remotely through a vulnerable ECU. How to evaluate the cyber security of in-vehicle ECUs has become an important issue. Current Threat Analysis and Risk Assessment (TARA) only carries out theoretical analysis on the potential threats and risks faced by the vehicle in the conceptual design phase of the lifecycle, but lacks the details of actual security evaluation. In this paper, we proposed a Cyber Security Evaluation Framework (CSEF) to independently evaluate the security of the in-vehicle ECUs, which is composed of the asset identification, the threat analysis, the risk assessment, and the security test. The proposed CSEF is applied to a pre-installed On-Bord Unit (OBU) to provide a use case. The use case show that the proposed CSEF is able to figure out assets, threats, risks behind threats, and vulnerabilities of OBU, playing an important role in guiding others to conduct security evaluation. Moreover, CSEF can be extended to evaluate the cyber security of other critical ECUs, such as the Telematic Box, the infotainment units, and the gateway.