Connected Vehicles (CVs), an integral part of the future of intelligent transportation systems, use communication and sensing technologies to communicate among vehicles and infrastructure. However, as vehicles become interconnected, the vulnerability of their components to anomalies and deliberate malicious activity increases. In both cases, it is vital to detect and exclude anomalous data from the decision-making process. While deep learning techniques are gaining popularity for anomaly detection due to their adaptability, they are computationally expensive and require long training times. To overcome this challenge, this paper uses a directed acyclic graph (DAG) based distributed ledger technique and combines it with social psychology principles of ability, integrity, and benevolence to calculate the reputation of vehicles. We introduce the probability of malevolence, a measure of quality, which is a function of the error measurements (between ground truth and reported values) and reputation metrics. We introduce various anomalies such as bias, noise, short, multi-short, drift, multi-drift, stuck-at, and parasite chain attack in the simulated data from the Intelligent Driver Module framework on road topology such as uphill, ring, on-ramp, off-ramp, and road-works to validate the efficacy of the proposed framework in identifying the anomalies. Simulation results show that the malevolence factor serves as an efficient metric for automatically determining the types of anomalies in the CV network.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Social Psychology Inspired Distributed Ledger Technique for Anomaly Detection in Connected Vehicles


    Contributors:


    Publication date :

    2023-07-01


    Size :

    2373511 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Distributed ledger system for modular vehicles

    GHANNAM MAHMOUD YOUSEF | European Patent Office | 2022

    Free access

    Anomaly Detection for Connected and Automated Vehicles: Accident Analysis

    Girdhar, Mansi / Hong, Junho / You, Yongsik et al. | IEEE | 2023


    Systematic Literature Review: Anomaly Detection in Connected and Autonomous Vehicles

    Solaas, John Roar Ventura / Mariconti, Enrico / Tuptuk, Nilufer | IEEE | 2025


    Anomaly Detection using IoT Sensor-Assisted ConvLSTM Models for Connected Vehicles

    Zekry, Ahmed / Sayed, Ahmed / Moussa, Mohamed et al. | IEEE | 2021


    Anomaly Detection in Connected and Automated Vehicles using an Augmented State Formulation

    Wang, Yiyang / Masoud, Neda / Khojandi, Anahita | IEEE | 2020