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      Addressing Polymorphic Attack Strategies with Misbehavior Detection for ITS

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      research-article
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      ScienceOpen Preprints
      ScienceOpen
      MDS, cITS, VANET, Deep Learning.
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            Abstract

            Cooperative Intelligent Transportation Systems (cITSs) is one of the Internet of Things (IoT) applications whose purpose is to improve road safety and traffic efficiency. Within this system, vehicles can communicate with one another by establishing a Vehicular Ad-Hoc Network (VANET) along the road section. Although such connectivity facilitates the exchange of information related to road safety and traffic efficiency, it puts the vehicles at risk in that an attacker could compromise one or more vehicles and use them to share false information causing congestions and/or life-threatening accidents. Although several studies tried to address this issue, they assume that the network topology and/or attack behavior is stationary, which is not realistic as the cITS is dynamic in nature and the attackers may have the ability and resources to change their behavior continuously. Therefore, these assumptions are not suitable and lead to low detection accuracy and high false alarms. To this end, this paper proposes a misbehavior detection model that can cope with the dynamicity of both cITS topology and attack behavior. The model starts by addressing the issue of missing data that happensat the early stages of the model formation after a topology change. Then, the deep learning approach is used to select the discriminative features used to train. We expect that the proposed model will help to overcome the limitations of related solutions by detecting attacks that change their behavior continuously

            Content

            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            10 February 2022
            Affiliations
            [1 ] Computer Science Department, University of Idaho, Moscow, ID, USA
            Author notes
            Article
            10.14293/S2199-1006.1.SOR-.PPVNX1G.v1
            68d9978d-f1b3-46e1-8e57-3fd493e12c60

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .


            The datasets generated during and/or analysed during the current study are available in the repository: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj
            Computer science,Mathematics
            MDS, cITS, VANET, Deep Learning.

            References

            1. Almalki Sultan Ahmed, Sheldon Frederick. Prospectus: An Online Polymorphic Attack Detection Model for Intelligent Transportation Systems. 2020 International Conference on Computational Science and Computational Intelligence (CSCI). 2020. IEEE. [Cross Ref]

            2. Almalki Sultan Ahmed, Sheldon Frederick T.. Deep Learning to Improve False Data Injection Attack Detection in Cooperative Intelligent Transportation Systems. 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). 2021. IEEE. [Cross Ref]

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