Attacks as Distributed Denial of Service (DDoS) are ones of the most frequent vehicle cybersecurity threats. In this paper, we propose a Machine Learning-based Intrusion Detection System (IDS) for monitoring network traffic and detecting abnormal activities. This IDS framework integrates streaming engines for big data analytics, management and visualization. A Vehicular ad-hoc network (VANET) topology of multiple connected nodes with mobility capability is simulated in the Mininet-Wifi environment. Real-time data is collected using the sFlow technology and transmitted from the simulator to our proposed IDS framework. We have achieved high detection accuracy results by training the Random Forest as the classifier to label out the anomalous flows. Additionally, the network throughput has been evaluated and compared with and without deploying the proposed IDS. The results verify the system is a lightweight solution by bringing little burden to the network.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Machine Learning-Based Intrusion Detection System for Big Data Analytics in VANET


    Contributors:
    Zang, Mingyuan (author) / Yan, Ying (author)


    Publication date :

    2021-04-01


    Size :

    3105658 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A Machine Learning Framework for Intrusion Detection in VANET Communications

    Ben Rabah, Nourhene / Idoudi, Hanen | Springer Verlag | 2022


    NDNIDS: An Intrusion Detection System for NDN Based VANET

    Manimaran, Praveensankar / P, Arun Raj Kumar | IEEE | 2020



    Machine Learning Algorithm based VANET Traffic Management System

    Suganyadevi, K. / Swathi, V. / Santhiya, T. et al. | IEEE | 2023