CT2‐MDS: Cooperative trust‐aware tolerant misbehaviour detection system for connected and automated vehicles

Liu, Ying; Xue, Hongwei; Zhuang, Weichao; Wang, Faan; Xu, Liwei; Yin, Guodong

doi:10.1049/itr2.12139

Cited by 17 publications

(4 citation statements)

References 37 publications

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“…Liu et al 81 suggested a new cooperative trust‐aware tolerant misbehavior detection system (CT2‐MDS) to protect the transmitted sensitive communications between connected and automated vehicles. First, a network model for the V2X system is constructed, and various common forms of assaults are simulated.…”

Section: Technical Analysis Of Connectivity In Vanetsmentioning

confidence: 99%

A systematic literature review of vehicular connectivity and V2X communications: Technical aspects and new challenges

Souri,

Zarei,

Hemmati

et al. 2024

Int J Communication

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SummaryVehicular connectivity is a significant key contribution in vehicular ad‐hoc networks (VANETs). The inherent high dynamicity of vehicles is a serious challenge that affects automotive connectivity and disrupts inter‐vehicle communications stability. Vehicle‐to‐everything (V2X) communication enables vehicles to connect to any other networks and facilitates the evolutionary trend of automotive innovations toward autonomous internet‐enabled intelligent vehicles. In this article, we provide a systematic literature review (SLR) for vehicular connectivity and V2X communications by investigating 58 research articles from 2018 to 2023 in VANETs. We propose six fundamental research questions (RQs) and a taxonomy to systematize the study of the baseline articles and answer the crucial RQs. This SLR can be helpful for researchers as a complete study focusing on V2X analysis, making it possible to achieve Technical Aspects and New challenges.

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Section: Technical Analysis Of Connectivity In Vanetsmentioning

confidence: 99%

A systematic literature review of vehicular connectivity and V2X communications: Technical aspects and new challenges

Souri,

Zarei,

Hemmati

et al. 2024

Int J Communication

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“…The authors evaluated the proposed approach using a real-world vehicular network traffic dataset and compared it to traditional anomaly detection methods. The authors of [23] proposed a cooperative trust-aware tolerant misbehavior detection system (CT2-MDS) to detect misbehavior in a cooperative environment. They used trust management and machine learning techniques to detect misbehavior.…”

Section: Related Workmentioning

confidence: 99%

Single and Mixed Sensory Anomaly Detection in Connected and Automated Vehicle Sensor Networks

Kim,

Ojo,

Krichen

et al. 2024

Electronics

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Connected and automated vehicles (CAVs), integrated with sensors, cameras, and communication networks, are transforming the transportation industry and providing new opportunities for consumers to enjoy personalized and seamless experiences. The fast proliferation of connected vehicles on the road and the growing trend of autonomous driving create vast amounts of data that need to be analyzed in real time. Anomaly detection in CAVs refers to identifying any unusual or unforeseen behavior in the data generated by vehicles’ various sensors and components. Anomaly detection aims to identify any unusual behavior that might indicate a problem or a malfunction in the vehicle. To identify and detect anomalies efficiently, a method must deal with noisy data, missing data, dynamic frequency data, and low- and high-magnitude data, and it must be accurate enough to detect anomalies in a dynamic sensor streaming environment. Therefore, this paper proposes a fast and efficient hard-voting-based technique named FT-HV, comprising three fine-tuned machine learning algorithms to detect and classify anomaly behavior in CAVs for single and mixed sensory datasets. In experiments, we evaluate our approach on the benchmark Sensor Anomaly dataset that contains data from various vehicle sensors at low and high magnitudes. Further, it contains single and mixed anomaly types that are challenging to detect and identify. The results reveal that the proposed approach outperforms existing solutions for detecting single anomaly types at low magnitudes and detecting mixed anomaly types in all settings. Furthermore, this research is envisioned to help detect and identify anomalies early and efficiently promote safer and more resilient CAVs.

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“…They pointed out that there is a lack of uniform evaluation metrics for performance measurements and that the trade-off between computational complexity and detection efficiency should be properly leveraged. Since a single machine learning method often has limitations while dealing with multiclass classification problems, Liu et al [23] combined several classifiers through a tree structure to improve classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Since a single machine learning method often has limitations while dealing with multi‐class classification problems, Liu et al. [23] combined several classifiers through a tree structure to improve classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Cyberattacks on connected automated vehicles: A traffic impact analysis

Sun

Liu

et al. 2022

IET Intelligent Trans Sys

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Cybersecurity has become one of the major challenges for Connected Automated Vehicles. Previous work in this field mostly focused on the detection of and defence against Connected Automated Vehicle‐related cyberattacks. Using performance measures collected from traffic micro‐simulations, the study analysed the traffic impacts of cyberattacks on Connected Automated Vehicles. Two types of adversary models, namely, time‐delay attacks and disturbance attacks, were applied to the simulated traffic on freeway segments and un‐signalised intersections, respectively. The effects of various cyberattacks were evaluated based on safety indicators, including Time‐To‐Collision, Deceleration Rate to Avoid Collision, and efficiency indicators, including speed and flow‐density diagrams. The results revealed that both attacks increase the risk and severity of collisions on freeway segments and un‐signalised intersections. Location‐based time‐delay attacks will result in significant deceleration, congestion and reduction in road throughput. Disturbance attacks not only cause congestion but also result in frequent acceleration/deceleration and uneven distribution of traffic density. The impacts of attacks are more severe on heavy traffic. At un‐signalised intersections, location‐based disturbance attacks lead to a significantly increased risk of right‐angled collisions. The results could help better understand the effects of Connected Automated Vehicle‐related cyberattacks and shed light on proactive defence against such attacks.

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CT2‐MDS: Cooperative trust‐aware tolerant misbehaviour detection system for connected and automated vehicles

Cited by 17 publications

References 37 publications

A systematic literature review of vehicular connectivity and V2X communications: Technical aspects and new challenges

A systematic literature review of vehicular connectivity and V2X communications: Technical aspects and new challenges

Single and Mixed Sensory Anomaly Detection in Connected and Automated Vehicle Sensor Networks

Cyberattacks on connected automated vehicles: A traffic impact analysis

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