The rapid evolution of modern automobiles into intelligent and interconnected entities presents new challenges in cybersecurity, particularly in Intrusion Detection Systems (IDS) for In-Vehicle Networks (IVNs). This survey paper offers an in-depth examination of advanced machine learning (ML) and deep learning (DL) approaches employed in developing sophisticated IDS for safeguarding IVNs against potential cyber-attacks. Specifically, we focus on the Controller Area Network (CAN) protocol, which is prevalent in in-vehicle communication systems, yet exhibits inherent security vulnerabilities. We propose a novel taxonomy categorizing IDS techniques into conventional ML, DL, and hybrid models, highlighting their applicability in detecting and mitigating various cyber threats, including spoofing, eavesdropping, and denial-of-service attacks. We highlight the transition from traditional signature-based to anomaly-based detection methods, emphasizing the significant advantages of AI-driven approaches in identifying novel and sophisticated intrusions. Our systematic review covers a range of AI algorithms, including traditional ML, and advanced neural network models, such as Transformers, illustrating their effectiveness in IDS applications within IVNs. Additionally, we explore emerging technologies, such as Federated Learning (FL) and Transfer Learning, to enhance the robustness and adaptability of IDS solutions. Based on our thorough analysis, we identify key limitations in current methodologies and propose potential paths for future research, focusing on integrating real-time data analysis, cross-layer security measures, and collaborative IDS frameworks.

Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks

The Controller Area Network (CAN) is a widely used communication protocol in automobiles, but it is vulnerable to various types of attacks. To address this issue, researchers have been exploring the use of intrusion detection systems (IDS) for the CAN bus. Deep learning and machine learning have been proven to be powerful tools for detecting intrusions accurately and quickly. However, deep learning models require large amounts of data to achieve optimal performance, which can be challenging in the case of a CAN bus IDS. To overcome this challenge, we propose a novel machine learning-based IDS called CANPerFL that uses a personalized federated learning scheme to aggregate datasets from different car models. By building a universal model trained on a small amount of data from each manufacturer, we can provide global knowledge that can be transferred to improve the performance of each participant. To demonstrate the efficiency of the proposed model, we collected a real CAN dataset consisting of three different car models: KIA, BMW, and Tesla. The experimental results show that the proposed model increases F1 scores by 4% overall, compared to baselines. Moreover, the proposed system provides significant advantages when the local dataset of each participant is relatively small. According to our experiments, the proposed models can achieve F1 scores of more than 90% with at least 30k training samples on each client. Finally, we show empirically that each participant takes benefits from joining the CANPerFL system.

https://www.mdpi.com/2076-3417/13/11/6369/pdf?version=1684839149

CANPerFL: Improve In-Vehicle Intrusion Detection Performance by Sharing Knowledge

Autonomous vehicles and Intelligent Transport Systems (ITS) have started to become a reality in recent years. However, shortcomings of these early intelligent vehicles demonstrate a need to increase an intelligent vehicle’s perceptual bubble beyond the vehicle’s onboard sensors. Vehicle-to-Everything (V2X) communications is a technology intended to enable intelligent vehicles to increase their perceptive range via communication with the outside world, i.e., other vehicles, road users, and infrastructure. One of the critical layers of V2X communications is that of the underlying wireless access. There remains an open debate on which technology (or technologies) will ultimately be adopted as the de facto wireless access technology for V2X communications. Presently, a myriad of unconnected threads of work are being undertaken to this end, resulting in an unclear picture of the current state of wireless access for V2X communications. In this tutorial review, we collate current research on wireless access for V2X communications to showcase recent developments and the challenges facing candidate technologies. To allow the reader to engage with the debate, we present an overview of the field of V2X communications, its history, and standardisation. Following this, we provide an overview of the key candidate wireless access technologies that may enable V2X communications. Through this work, we aim to provide a comprehensive first step into the discipline of wireless access for V2X communications.

Wireless Access for V2X Communications: Research, Challenges and Opportunities

The Internet of Things (IoT) and advanced human-centred technologies have seen rapid expansion in smart city applications, including smart enterprises, homes, and vehicular networks. The IoT architecture underpins technological support for both human- and device-centric secure solutions, which are essential to the functioning of smart cities. Within this evolving landscape, Vehicle-to-Everything (V2X) technology has emerged as a key innovation, integrating vehicular communication with internet connectivity. This integration facilitates seamless data exchange between vehicles, infrastructure, and other entities, contributing to enhanced road safety, optimized traffic flow, reduced emissions, and the development of more intelligent transportation systems. As V2X technology becomes increasingly widespread, ensuring the security and privacy of the information exchanged within these networks is critical. While much of the existing research has concentrated on general IoT security frameworks and standalone vehicular communication protocols, our study addresses the unique security challenges inherent in V2X environments. This paper identifies and examines various security threats, including data privacy breaches, malicious attacks, and traffic manipulation, and explores strategies to mitigate these risks within V2X communications. In addition, this paper investigates the potential of blockchain technology as an innovative solution for enhancing the security and trustworthiness of information exchanges in V2X networks. The paper also includes a case study that presents a proposed solution demonstrating how blockchain technology could strengthen the security and integrity of data transfers within V2X systems. Through comprehensive analysis, evaluation, and recommendations for future research, this work contributes to the current understanding of secure data exchange in V2X networks.

Exploring Secure V2X Communication Networks for Human-centric Security and Privacy in Smart Cities

Smart Cities and Transportation Based Vehicle-To-Vehicle Communication and Cyber Security Analysis Using Machine Learning Model in 6G Network

Sixth-generation (6G) communication is emerging as a seamless and massive connection of almost all digital devices. Vehicles, which are extensively linked with human mobility, must keep up with a certain technological pace to maintain compatibility with the 6G era. 6G is also expected to potentially revolutionize Vehicle-to-Everything (V2X) communication. However, this modernization will surface several security challenges in the complex heterogeneous architecture of V2X communication in 6G. Similarly, the expansion of V2X is expected to introduce its own unconventional security risks and vulnerabilities. Therefore, the current paper aims to provide an overview of the security challenges associated with and solutions for V2X communication in the upcoming 6G era to visualize the future of this research domain. This paper discusses the architecture and standards utilized in 6G-enabled V2X communications and provisions a comprehensive analysis of V2X security in the Confidentiality, Integrity, Availability, Authentication, and Access Control (CIA3) domains. Through this process, we analyze the impact of the emerging technological concepts of Blockchain and Federated Learning (FL) in 6G-enabled V2X communication. Therefore, we propose a Blockchain-enabled FL-based generic security architecture for V2X communication in 6G networks. Ultimately, this review highlights the key lessons that have been learned and the future research directions in the domain of security of V2X communications in the 6G including Privacy in 3D Fog Computing, Privacy in Augmented Reality, Secure Software Defined Networking (SDN), Physical Layer Security in the THz Spectrum, SUMO (Simulation of Urban MObility), and Intrusion Detection using AI.

Security of 6G-Enabled Vehicle-to-Everything Communication in Emerging Federated Learning and Blockchain Technologies

The Controller Area Network (CAN) protocol is widely used in automotive and industrial applications for communication. However, the lack of authentication and encryption in CAN bus networks has made them vulnerable to cyberattacks. This study investigated the effectiveness of different intrusion detection models in accurately classifying attacks, such as Denial-of-Service (DoS) attacks, fuzzing, and replay attacks. A labeled dataset was created using a methodology that uses the CAN ID sequence, time gap, and hamming distance between hexadecimal strings of equal length. The resulting dataset was segmented and converted into heat maps that were input to deep learning models such as VGG-16, AlexNet, and ResNet-50. The study provides valuable insights for developing more robust security measures for in-vehicle networks. However, recent research has shown that intrusion detection systems need to be developed individually for each vehicle, taking into account the unique data characteristics of the vehicle. Therefore, this paper proposes to implement universal IDS by using three types of CNN architectures to find the best one that is suitable for all types of attacks with high accuracy. 

Universal Intrusion Detection System on In-Vehicle Network

Wavelet Transform Based PID Sequence Analysis for IDS on CAN Protocol

As technology advances, the mechanical components of vehicles are being replaced by microcontrollers known as Electronic Control Units (ECUs). These ECUs connect to form an In-Vehicle Network, and they communicate control messages to each other in order to control the vehicle. There are several message protocols that can be used for IVN communication, but the most used protocol is the Controller Area Network (CAN) message. While CAN messages offer flexibility and cost-effectiveness to the network, their lack of security can make the network vulnerable to attacks, which can have serious consequences for both vehicles and drivers. In order to prevent such vulnerabilities, researchers have studied security systems such as Intrusion Detection Systems (IDS). However, the lack of availability and integrity of datasets can lead to misdetection, making it difficult to evaluate methods effectively. Therefore, we collected IVN messages based on several scenarios via collection tool. In this paper, we analyze CAN messages of each scenario and put into ML and DL models as testing dataset, and observe the results of each scenario. These findings shows miscalculation of the data collection scenario could lead to misdetection. 

A Novel Attack Scenario Dataset Collection for Intrusion Detection System in CAN Network

Intrusion detection systems are critical to maintaining network security, especially for onboard vehicle networks. However, the complexity of existing systems often requires high computational power, making them impractical for many applications. In this study, we propose a simple yet effective approach for intrusion detection based on polynomial feature processing and a lightweight deep learning model. Our approach achieves significantly higher accuracy than other computationally intensive deep learning models, making it a practical solution for intrusion detection in low-resource environments. Our proposed method can be easily implemented and integrated into existing systems, providing a lightweight yet effective solution for network security. 

A Lightweight Intrusion Detection System on In-Vehicle Network Using Polynomial Features

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