The Internet of Vehicles (IoV) plays a crucial role in intelligent transportation systems (ITS) by enabling communication between interconnected vehicles and supporting infrastructure. Connected vehicles utilize basic safety messages (BSMs) to exchange kinematic data, such as vehicle acceleration, velocity, position, and direction, with neighbouring nodes in the ITS network to enhance road safety. However, these BSMs are susceptible to various security attacks, which disrupt the collaborative functionality of ITS, potentially resulting in accidents or traffic congestion. The scientific community has proposed numerous security mechanisms to protect BSMs. The majority of these assessments have been conducted utilizing either the vehicular reference misbehaviour (VeReMi) dataset or the VeReMi extension dataset. These datasets are specifically designed for the Luxembourg SUMO Traffic (LuST) scenario and are suitable for only evaluating misbehaviour detection methods within a European ITS context. However, there is a notable scarcity of publicly accessible misbehaviour datasets that faithfully depict Indian ITS scenarios. To overcome this limitation, we introduce a new scenario, i.e., the Ahmedabad SUMO Traffic (AhmST) scenario, based on the city of Ahmedabad in Gujarat, India. Moreover, we also introduce the Indian dataset for misbehaviour analysis (AhmST). The proposed dataset includes cases of false data injections affecting the vehicle position, heading, and speed information within BSMs. Finally, we compare the AhmST dataset with recent datasets, assess the proposed dataset using various machine learning techniques and present an optimized model with improved accuracy.


    Access

    Download


    Export, share and cite



    Title :

    Indian SUMO traffic scenario-based misbehaviour detection dataset for connected vehicles


    Contributors:


    Publication date :

    2025




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    ToST: Tokyo SUMO Traffic Scenario

    Yamazaki, Yuji / Tamura, Yasumasa / Defago, Xavier et al. | IEEE | 2023


    POSTER: Anomaly-based misbehaviour detection in connected car backends

    Berlin, Olga / Held, Albert / Matousek, Matthias et al. | IEEE | 2016


    FraST: Frankfurter Kreuz SUMO Traffic Scenario

    Willecke, Alexander / Garlichs, Keno / Schulze, Fynn et al. | IEEE | 2023


    Misbehaviour Detection Mechanisms in Internet of Vehicles

    Lv, Chenchen / Zhang, Yu’ang / Cao, Yue et al. | Springer Verlag | 2023


    InTAS -- The Ingolstadt Traffic Scenario for SUMO

    Lobo, Silas C. / Neumeier, Stefan / Fernandez, Evelio M. G. et al. | ArXiv | 2020

    Free access