Automotive Networks Research Articles

Benchmarking frameworks and comparative studies of Controller Area Network (CAN) intrusion detection systems: A review

The development of intrusion detection systems (IDS) for the in-vehicle Controller Area Network (CAN) bus is one of the main efforts being taken to secure the in-vehicle network against various cyberattacks, which have the potential to cause vehicles to malfunction and result in dangerous accidents. These CAN IDS are evaluated in disparate experimental conditions that vary in terms of the workload used, the features used, the metrics reported, etc., which makes direct comparison difficult. Therefore, there have been several benchmarking frameworks and comparative studies designed to evaluate CAN IDS in similar experimental conditions to understand their relative performance and facilitate the selection of the best CAN IDS for implementation in automotive networks. This work provides a comprehensive survey of CAN IDS benchmarking frameworks and comparative studies in the current literature. A CAN IDS evaluation design space is also proposed in this work, which draws from the wider CAN IDS literature. This is not only expected to serve as a guide for designing CAN IDS evaluation experiments but is also used for categorising current benchmarking efforts. The surveyed works have been discussed on the basis of the five aspects in the design space – namely, IDS type, attack model, evaluation type, workload generation, and evaluation metrics – and recommendations for future work have been identified.

BIDS: An efficient Intrusion Detection System for in-vehicle networks using a two-stage Binarised Neural Network on low-cost FPGA

Automotive networks are crucial for ensuring safety as the number of Electronic Control Units (ECUs) grows to support vehicle intelligence. The Controller Area Network (CAN) is commonly used for efficient in-vehicle communication among ECUs. However, its broadcast nature and lack of a dedicated security layer make it vulnerable to attacks. This paper proposes a novel CAN bus Intrusion Detection System (IDS), named BNN-based IDS (BIDS), which efficiently provides both unknown attack detection and known attack classification using a hierarchical two-stage Binarised Neural Network (BNN) and Generative Adversarial Network (GAN). BIDS was validated on three datasets, and its implementation achieves an average inference time of less than 0.170 ms with minimal resource utilisation on a low-cost Field Programmable Gate Array (FPGA). This rapid inference speed enables real-time inference on individual CAN messages using a sliding window technique, eliminating the need to wait for multiple accumulated CAN messages required for data preprocessing. Evaluation metrics demonstrate that our IDS achieves high accuracy in both identifying unseen attacks and categorising known attacks. Furthermore, our FPGA implementation consumes merely 2.09 W, which is a 57% reduction compared to a cutting-edge FPGA-based IDS that is capable of detecting unknown attacks using the same dataset.

A privacy-preserving Self-Supervised Learning-based intrusion detection system for 5G-V2X networks

In light of the ongoing transformation in the automotive industry, driven by the adoption of 5G and the proliferation of connected vehicles, network security has emerged as a critical concern. This is particularly true for the implementation of cutting-edge 5G services such as Network Slicing (NS), Software Defined Networking (SDN), and Multi-access Edge Computing (MEC). As these advanced services become more prevalent, they introduce new vulnerabilities that can be exploited by cyber attackers. Consequently, Network Intrusion Detection Systems (NIDSs) are pivotal in safeguarding vehicular networks against cyber threats. Still, their efficacy hinges on extensive data, which often contains sensitive and confidential information such as vehicle positions and owner’s behaviors, raising privacy concerns. To address this issue, we propose a Privacy-Preserving Self-Supervised Learning (SSL) based Intrusion Detection System for 5G-V2X networks. The majority of works in the literature relying on Federated Learning (FL) and often overlook data labeling on the end devices. Our methodology leverages SSL to pre-train NIDSs using unlabeled data. Post-training is then performed with a minimal amount of labeled data, which can be carefully crafted by an expert. This novel technique allows the training of NIDSs with huge datasets without compromising privacy, consequently enhancing the efficacy of cyber-attack protection. Our innovative SSL pre-training methodology has yielded remarkable results, demonstrating a substantial improvement of up to 9% in accuracy across a diverse range of training dataset sizes, including scenarios with as few as 200 data samples. Our approach highlights the potential to enhance automotive network security significantly, showcasing groundbreaking achievements that set a new standard in the field of automotive cybersecurity.

Authentication with privacy-preserving scheme for 5G-enabled vehicular networks

The 5G-enabled vehicular network is an innovative technology that has promise for intelligent transportation systems. It enables the transmitting of messages about traffic that deliver the most recent information on congestion, road conditions, and driving surroundings. The communication channel used by vehicle networks is inherently open, which unfortunately exposes the system to privacy and security concerns. To solve the problems of deploying a safe vehicular network, some academics have put forth plans. However, a number of current methods have significant computational or communication overhead costs. To solve this problem, an efficient and secure authentication with a privacypreserving (ES-APP) scheme established elliptic curve encryption is introduced. With the proposed ES-APP, the data signed and verified for vehicle-to-vehicle and vehicle-to-infrastructure modes in the 5G-based vehicular network are more effective. The ES-APP scheme’s goal is to meet the criteria for the security and privacy of the 5G-enabled automotive network. Ultimately, this work discusses the critical survey of the existing studies and the expected outcome for the ESAPP scheme and further works in the 5G-enabled vehicular network.

Design and Evaluation of a 2Mbps SEC Transceiver for Automotive Networks

Design and Evaluation of a 2Mbps SEC Transceiver for Automotive Networks

A Blockchain-Based Mutual Authentication Method to Secure the Electric Vehicles’ TPMS

Despite the widespread use of Radio Frequency Identification (RFID) and wireless connectivity such as Near Field Communication (NFC) in electric vehicles, their security and privacy implications in Ad-Hoc networks have not been well explored. This paper provides a data protection assessment of radio frequency electronic system in the Tire Pressure Monitoring System (TPMS). It is demonstrated that eavesdropping is completely feasible from a passing car, at an approximate distance up to 50 meters. Furthermore, our reverse analysis shows that the static <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -bit signatures and messaging can be eavesdropped from a relatively far distance, raising privacy concerns as a vehicles' movements can be tracked by using the unique IDs of tire pressure sensors. Unfortunately, current protocols do not use authentication, and automobile technologies hardly follow routine message confirmation so sensor messages may be spoofed remotely. To improve the security of TPMS, we suggest a novel ultra-lightweight mutual authentication for the TPMS registry process in the automotive network. Our experimental results confirm the effectiveness and security of the proposed method in TPMS.

Driving the Future: Design and Implementation of an Advanced Smart Car Network Simulation Platform for Education

— Incorporating smart car network education into an academic setting often faces a substantial cost barrier. Traditionally, creating a realistic learning environment involved significant investments, such as procuring an actual smart car or specialized automotive-grade testing equipment. However, these approaches pose challenges, especially in resource-constrained educational contexts. This project addresses these challenges by introducing an innovative solution—a smart car education platform. Our objective is to design and implement this platform using a limited number of microcontrollers and components extracted from modern vehicles, enabling the creation of a lifelike simulation of genuine smart car networks. By adopting this approach, we enhance accessibility for students interested in gaining practical insights into automotive networks, offering a more pragmatic and cost-effective educational pathway.

A Universal Source DC–DC Boost Converter for PEMFC‐Fed EV Systems With Optimization‐Based MPPT Controller

Conventional energy networks produce energy with less efficiency. Also, these source’s development costs and size are more. So, the world is focusing on renewable energy networks for energy production to the consumer. In this work, a proton exchange membrane fuel stack (PEMFS) technology is selected for energy feeding to the hydrogen vehicle. The merits of this stack are more abundant, faster fuel stack operational response, and more efficient for electrical automotive networks. However, the fuel stack’s energy production is nonlinear and its operational point varies concerning the fuel stack device operating temperature. The particle swarm optimized adaptive network‐based fuzzy inference system (PSO‐ANFIS) is proposed in this work to find the operational point of the fuel cell network. The features of this hybrid methodology are the low number of iteration values required, low convergence time, low‐level dependence on the fuel stack, and high compliance for the quick deviations of the fuel system temperature. The operating efficiency and tracking time of the proposed maximum power point tracking (MPPT) controller are 95.60% and 0.1089 s. Another issue of the fuel cell is high output current generation and less voltage production. This condition is happening in the fuel cell because of its chemical reaction dynamics, internal resistance of the cell, and electrochemical potential. Due to this excess current flow in the fuel cell, the direct fuel stack‐fed electrical networks face the issue of high power conduction losses. To reduce the power conduction losses of the system, a single‐switch power circuit is used to reduce fuel source current, thereby optimizing the excessive power losses of the system. The whole fuel stack energy production network is analyzed by selecting the MATLAB Window.

Hierarchical Clustering Based on Dendrogram in Sustainable Transportation Systems

Each group in a data-driven automobile network has its cluster head. A group can communicate with each other and members of other groups once it has been founded. Vehicles belonging to each group near the other group allow intergroup communication. Because nodes in automotive networks move so quickly, routing in these networks is a complex problem to solve. Each cluster in hierarchical clustering can be partitioned into multiple sub-clusters. Put another way, and the data is stored in a cluster, which is then divided into more clusters. The data is stored directly in separate clusters in non-hierarchical approaches. A dendrogram is a type of hierarchical tree. We anticipate increasing information sharing in clusters by properly clustering vehicles on the road and establishing clusters of the desired size in the relevant dendrogram. We can select clusters of the necessary extent and compare the Quality of Service (QoS) network’s outcomes by breaking the dendrogram at different levels. The findings reveal that the suggested method outperforms AIVISN in delay, PDR, overhead, and Drooped packets compared to AIVISN, 7.12%, 12.21%,8.32%, and 7.34%, respectively.

Improving the Performance of Delay-sensitive Multimedia Computation in Automotive Networks with DP-ERACOM using CloudSim Simulator

Abstract: We propose a Dynamic Priority-based Efficient Resource Allocation and Computing (DP-ERACOM) scheme for processing delay-sensitive multimedia computation in automotive networks. The proposed scheme breaks down each multimedia task into four smaller tasks and dynamically distributes resources based on the priority of the multimedia tasks. The three main processing and computing units in the proposed scheme are the load manager, compute cluster unit, and transmission unit. We evaluate the performance of the proposed scheme using CloudSim simulator. The results show that the proposed scheme can significantly improve the performance of delay-sensitive multimedia computation in automotive networks.

A lightweight-X-authentication protocol over automotive gateway

One of the most dynamic industries nowadays is the automotive industry. It has dramatically changed from safe, comfortable, and equipped with many entertainment systems to connected autonomous vehicles during the last decade. Although the “Connected Autonomous vehicles” trend dramatically influences the economy, it increases automation and connectivity. Consequently, vehicles can be considered an Internet of Things (IoT) network. Accordingly, the cyberattack surface is more significant, with many vulnerabilities. Considering the fundamental security weakness of automotive onboard networks, besides their superimposing, we firmly believe that a unified security solution is required to resolve the current automotive security problem. This paper proposes an automotive security solution called Generic Automotive Security Suit (GASS). (GASS) is designed to be a practical security solution deployed in the complicated automotive environment. In addition, it defines a standard secure inter-communication between onboard networks. We also introduce the Lightweight Generic Authentication Protocol (LXAP) as the building block of the GASS. We address different security scenarios which face the industry nowadays. The new proposed system does not affect the complexity level of the automotive network. In addition, our simulation results have shown that the LXAP enables security preservation over vehicles’ complex networks.

Network dynamics and action space

PurposeIn today’s business settings, most firms strive to closely integrate their resources and activities with those of their business partners. However, these linkages tend to create lock-in effects when changes are needed. In such situations, firms need to generate new space for action. The purpose of this paper is twofold: analysis of potential action spaces for restructuring; and examination of how action spaces can be exploited and the consequences accompanying this implementation.Design/methodology/approachNetwork dynamics originate from changes in the network interdependencies. This paper is focused on the role of the three dual connections – actors–activities, actors–resources and activities–resources, identified as network vectors. In the framing of the study, these network vectors are combined with managerial action expressed in terms of networking and network outcome. This framework is then used for the analysis of major restructuring of the car industries in the USA and Europe at the end of the 1900s.FindingsThis study shows that the restructuring of the car industry can be explained by modifications in the three network vectors. Managerial action through changes of the vector features generated new action space contributing to the transition of the automotive network. The key to successful exploitation of action space was interaction – with individual business partners, in triadic constellations, as well as on the network level.Originality/valueThis paper presents a new view of network dynamics by relying on the three network vectors. These concepts were developed in the early 1990s. This far, however, they have been used only to a limited extent.

Smart optimization in 802.11p media access control protocol for vehicular ad hoc network

&lt;span lang="EN-US"&gt;The innovative idea presented in this research is that advancements in automotive networks and embedded devices can be used to assess the impact of congestion control on throughput and packet delivery ratio (PDR), or so-called multimedia content delivery. Vehicle networking and the distribution of multimedia content have become essential factors in getting packets to their intended recipients due to the availability of bandwidth. Vehicle-to-infrastructure (V2I) communication systems are crucial in vehicular ad hoc networks (VANETs), which permit vehicles to connect by distributing and delivering traffic data and transmission packet schemes. High levels of mobility and changing network topology necessitate dispersed monitoring and execution for congestion control. The amount of traffic congestion for packet transfers could be reduced by enhancing congestion management in terms of throughput and PDR percentages. In a highway setting, the Taguchi approach has been used to optimize the parameters for congestion control. Based on throughput and PDR performance measures, this technique minimizes superfluous traffic information and lowers the likelihood of network congestion. The simulation results have shown that the proposed approach performs better since it increases network performance while effectively utilizing bandwidth. The effectiveness of the suggested technique is evaluated using a typical VANETs scenario for V2I communication while driving on a highway.&lt;/span&gt;

Chebyshev Polynomial-Based Fog Computing Scheme Supporting Pseudonym Revocation for 5G-Enabled Vehicular Networks

The privacy and security of the information exchanged between automobiles in 5G-enabled vehicular networks is at risk. Several academics have offered a solution to these problems in the form of an authentication technique that uses an elliptic curve or bilinear pair to sign messages and verify the signature. The problem is that these tasks are lengthy and difficult to execute effectively. Further, the needs for revoking a pseudonym in a vehicular network are not met by these approaches. Thus, this research offers a fog computing strategy for 5G-enabled automotive networks that is based on the Chebyshev polynomial and allows for the revocation of pseudonyms. Our solution eliminates the threat of an insider attack by making use of fog computing. In particular, the fog server does not renew the signature key when the validity period of a pseudonym-ID is about to end. In addition to meeting privacy and security requirements, our proposal is also resistant to a wide range of potential security breaches. Finally, the Chebyshev polynomial is used in our work to sign the message and verify the signature, resulting in a greater performance cost efficiency than would otherwise be possible if an elliptic curve or bilinear pair operation had been employed.

An efficient key validation mechanism with VANET in real-time cloud monitoring metrics to enhance cloud storage and security

A vehicle-specific network (VANET) in automotive networks is formed along the road unit. There are many challenges in quality management to avoid expensive RSUs and improve the quality management of VANET systems. Our proposed work proposes a new algorithmic programme for organizing the cluster structure and choosing cluster headers (CHs) compatible with VANET. Weighted Cluster Protocol (AWCP) teams nodes at random, so it reaches the best channel by optimizing network parameters. The proposed work with analysis of the AWCP EE-WOA model is an automotive network with selected speed and placement. VANET provides a list of vehicles that failed to interact in the preceding period. System administrators should routinely update the generated keys of all the systems' vehicles, and the system administrator should suspend or revoke the power of scenarios. This validation occurs in the cloud whenever the vehicle generates the best algorithm for identifying keys. The results show that the projected AWCP EE-WOA model outperforms the Weighted Cluster Protocol and AWCP Whale algorithmic program protocols in terms of improved encapsulation potency and movability with a maximum cluster efficiency of 96.84.

AI-based Intrusion Detection for Intelligence Internet of Vehicles

With the development of intelligent technologies, Internet of Things (IoT) opens up a new era in the field of automotive networks, namely, Internet of Vehicles (IoV). The main goal of IoV is to provide a secure and reliable network to vehicles so that users can enjoy various services. However, vulnerabilities and incomplete protection mechanisms have led to a proliferation of security threats against IoV networks. Intrusion detection technology is an effective protection solution for IoV security, especially when artificial intelligence (AI) technology has been introduced into intrusion detection study. This article first briefly introduces the concept and features of IoV, and then reviews the related research on AI-based IoV intrusion detection systems. Finally, we discuss the open challenges and future research directions.

Seamless CAN: A Novel Fault-Tolerant Algorithm and Its Modeling

Because there is always a demand for robust fault-tolerant automotive networks, recent works have focused particularly on replacing controller area networks (CANs) with more reliable protocols for time-critical bandwidth-demanding in-vehicle network (IVN) systems. However, as the automobile industry normally relies on proven technologies, an interim solution to fault-tolerance capability for CAN systems is still essential during the transition phase to other protocols. In this paper, we propose a novel fault-tolerant algorithm for the CAN protocol that we refer to as seamless CAN. Its operating concept is based on a high-availability seamless redundancy (HSR) implementation inside the vehicle network’s ring structure, and the fault tolerance is achieved by encapsulating the CAN frame inside the HSR frames and sending them in the ring topology. The simulation results showed that seamless CAN provided better performance regarding no frame loss in the case of link failures and up to ten times lower number of error occurrences.

Improving the quality of service by continuous traffic monitoring using reinforcement learning model in VANET

The growing number of automobiles on the road has now become a significant source of traffic, accidents, and pollution. Intelligent Transportation Systems (ITSs) could be the key to finding solutions that drastically reduce these issues. The linked vehicular networks channel is a fast-expanding topic for workflow management system and development. Traffic detection is a big issue on city streets. To make informed decisions in order to prevent traffic jams, one of the solutions is the Vehicular Ad-Hoc Network (VANET). We describe an approach for detecting traffic jams in both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, which provides vehicle drivers with multiple alternatives for determining the location of congestion, its size, and how to prevent becoming stuck in a traffic jam. The data are sent to the driver to assist him in making the appropriate selection. Delivering Quality of Service (QoS) for automotive networks is a difficult challenge due to the characteristics such as terms of transmission or high mobility, congested and fragmented channels, hardware defects, and a large number of vehicular devices. As a result, it is extremely desired to get and distribute resources efficiently. This research uses Reinforcement Learning with Decision-Making Model (RLDMM) method to enhance channel allocation. By using latency, Signal-to-Interference Ratio (SIR) and QoS, the available channel is initially determined. This method is used to determine suitable channel for platoon members. As a result, the proposed RLDMM achieves an SINR reduction of 29 Db, 69 kbps of throughput, 21.4% of collision probability with 4[Formula: see text]ms of latency.

Delay Boundary Analysis of RC Flows in the TTE Switch

With the rapid development of advanced driver assistance systems (ADASs) and infotainment systems, automotive networks have higher requirements for bandwidth and real-time than before. Time-triggered Ethernet (TTE) supports real-time time-triggered communication and event-triggered communication coexistence. TTE is used as a backbone network in intelligent connected vehicles to meet the growing real-time and reliability requirements. To design a real-time TTE switch, the time delay of data streams needs to be analyzed. Most of the recent works analyze end-to-end latency, which is usually based on a particular integration policy and ignores the impact of BE flows. In this paper, we analyze the delay bounds of RC flows in the TTE switch using delay reachability techniques. We consider the impacts of TT flows, RC flows and BE flows on RC flows based on three integration policies: shuffling, preemption and timely blocking, respectively. We obtain a secure upper limit of delay through analysis. Experiments illustrate the degree of influence of each factor on the delay bound of RC flow, which provides a reference for the optimal design of the TTE switch in intelligent connected vehicles.

Efficient Cryptographic Hardware for Safety Message Verification in Internet of Connected Vehicles

An important security requirement in automotive networks is to authenticate, sign, and verify thousands of short messages per second by each vehicle. This requirement mandates the use of a high speed Elliptic Curve Cryptography (ECC) hardware. The Residue Number Systems (RNS) provide a natural parallelism and carry-free operations that could speed-up long integer arithmetics of cryptographic algorithms. In this article, we propose a high-speed RNS Montgomery modular reduction units with parallel computing to reduce the latency of the field modular operations. We propose a fully RNS-based ECC scalar multiplication co-processor for NIST-P256r1 and Brainpool256r1 standard curves and improved the scalar multiplication speed using NAF and DBC numbering systems. Compared to the literature, our scheme provides faster computation without compromising the security level. The performance of our fully RNS-ECC point multiplication meets the requirements of the automotive industry.