Machine learning and blockchain technologies for cybersecurity in connected vehicles

Ahmad, Jameel; Zia, Muhammad Umer; Naqvi, Ijaz Haider; Chattha, Jawwad Nasar; Butt, Faran Awais; Huang, Tao; Xiang, Wei

doi:10.1002/widm.1515

Cited by 20 publications

(4 citation statements)

References 211 publications

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“…The security of future connected, and autonomous vehicles (CAVs) was discussed by Ahmad et al [5]. Their paper offered a thorough analysis of cybersecurity risks aimed at sensors and problems with multimodal sensor fusion.…”

Section: Literature Reviewmentioning

confidence: 99%

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Harnessing the power of blockchain to strengthen cybersecurity measures: a review

Turab,

Owida,

Al-Nabulsi

2024

IJEECS

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As the digital environment continues to evolve with the increasing frequency and complexity of cybersecurity threats, there is growing interest in using blockchain (BC) technology. BC is a technology with desirable properties such as decentralization, integrity, and transparency. The decentralized nature of BC eliminates single points of failure, reducing the vulnerability of critical systems to targeted attacks. The complex and rapidly evolving nature of cyber threats requires an earlier and adaptive approach. This review paper examined several papers collected from official websites. Focusing on using BC technology to improve cybersecurity, the main keywords of the review paper were BC technology, supply chain management, proof of work, and proof of stake. This review paper aims to investigate the security components through a threat assessment that compares the security of BC in different classes and real attack environments. It highlights the potential of BC to strengthen cybersecurity measures, citing unique features. The review paper also points out that there is a lack of focus on addressing security challenges related to computer data and digital systems and calling for a deeper discussion on problem-solving.

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Section: Literature Reviewmentioning

confidence: 99%

“…[4] A simulation approach for training in cyber operations, supporting with the cybersecurity framework's phases: identify, protect, detect, respond, and recover. [5] The security of future CAVs was discussed. [6] INCHAIN, an innovative architecture that improves data traceability and transparency by utilizing Blockchain technology.…”

Section: Appendixmentioning

confidence: 99%

Harnessing the power of blockchain to strengthen cybersecurity measures: a review

Turab,

Owida,

Al-Nabulsi

2024

IJEECS

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“…These can be intra-vehicle threats associated with engine control, transmission units, temperature control, and various Electronic Control Units (ECUs), or Vehicle-to-everything (V2X) threats, which include a spectrum of cyber threats, including DoS attacks, black-hole attacks, replay attacks, Sybil attacks, malware infiltration, falsified-information attacks, timing attacks, and impersonation attacks in V2X communications. Recent surveys [56,57] provide a detailed exploration of security vulnerabilities and their countermeasure strategies using blockchain and machine learning technologies in Connected Autonomous Vehicles (CAVs).…”

Section: Late Fusionmentioning

confidence: 99%

Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey

Alkashto,

Elewi

2024

Turkish Journal of Engineering

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The integration of blockchain and machine learning technologies has the potential to enable the development of more secure, reliable, and efficient autonomous car systems. Blockchain can be used to store, manage, and share the large amounts of data generated by autonomous vehicle various sensors and cameras, ensuring the integrity and security of these data. Machine learning algorithms can be used to analyze and fuse these data in real time, allowing the vehicle to make informed decisions about how to navigate its environment and respond to changing conditions. Thus, the combination of these technologies has the potential to improve the safety, performance, and scalability of autonomous car systems, making them a more applicable and attractive option for consumers and industry stakeholders. In this paper, all relevant technologies, such as machine learning, blockchain and autonomous cars, were explored. Various techniques of machine learning were investigated, including reinforcement learning strategies, the evolution of artificial neural networks and main deep learning algorithms. The main features of the blockchain technology, as well as its different types and consensus mechanisms, were discussed briefly. Autonomous cars, their different types of sensors, potential vulnerabilities, sensor data fusion techniques, and decision-making models were addressed, and main problem domains and trends were underlined. Furthermore, relevant research discussing blockchain for intelligent transportation systems and internet of vehicles was examined. Subsequently, papers related to the integration of blockchain with machine learning for autonomous cars and vehicles were compared and summarized. Finally, the main applications, challenges and future trends of this integration were highlighted.

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“…The Upstream 2022 report shows that over 900 CAV cybersecurity incidents occurred in 2021 alone [3]. This grim reality has drawn the close attention of many researchers and prompted them to explore in depth the security threats faced by CAVs to propose effective prevention and response strategies [4][5][6][7][8][9][10][11]. Among the many threats, Sybil attacks [12] are particularly challenging.…”

Section: Introductionmentioning

confidence: 99%

Sybil Attacks Detection and Traceability Mechanism Based on Beacon Packets in Connected Automobile Vehicles

Zhu,

Zeng,

Weng

et al. 2024

Sensors

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Connected Automobile Vehicles (CAVs) enable cooperative driving and traffic management by sharing traffic information between them and other vehicles and infrastructures. However, malicious vehicles create Sybil vehicles by forging multiple identities and sharing false location information with CAVs, misleading their decisions and behaviors. The existing work on defending against Sybil attacks has almost exclusively focused on detecting Sybil vehicles, ignoring the traceability of malicious vehicles. As a result, they cannot fundamentally alleviate Sybil attacks. In this work, we focus on tracking the attack source of malicious vehicles by using a novel detection mechanism that relies on vehicle broadcast beacon packets. Firstly, the roadside units (RSUs) randomly instruct vehicles to perform customized key broadcasting and listening within communication range. This allows the vehicle to prove its physical presence by broadcasting. Then, RSU analyzes the beacon packets listened to by the vehicle and constructs a neighbor graph between the vehicles based on the customized particular fields in the beacon packets. Finally, the vehicle’s credibility is determined by calculating the edge success probability of vehicles in the neighbor graph, ultimately achieving the detection of Sybil vehicles and tracing malicious vehicles. The experimental results demonstrate that our scheme achieves the real-time detection and tracking of Sybil vehicles, with precision and recall rates of 98.53% and 95.93%, respectively, solving the challenge of existing detection schemes failing to combat Sybil attacks from the root.

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Machine learning and blockchain technologies for cybersecurity in connected vehicles

Cited by 20 publications

References 211 publications

Harnessing the power of blockchain to strengthen cybersecurity measures: a review

Harnessing the power of blockchain to strengthen cybersecurity measures: a review

Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey

Sybil Attacks Detection and Traceability Mechanism Based on Beacon Packets in Connected Automobile Vehicles

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