In-vehicle network security plays a vital role in ensuring the secure information transfer between vehicle and Internet. The existing research is still facing great difficulties in balancing the conflicting factors for the in-vehicle network security and hence to improve intrusion detection performance. To challenge this issue, we construct a many-objective intrusion detection model by including information entropy, accuracy, false positive rate and response time of anomaly detection as the four objectives, which represent the key factors influencing intrusion detection performance. We then design an improved intrusion detection algorithm based on many-objective optimization to optimize the detection model parameters. The designed algorithm has double evolutionary selections. Specifically, an improved differential evolutionary operator produces new offspring of the internal population, and a spherical pruning mechanism selects the excellent internal solutions to form the selected pool of the external archive. The second evolutionary selection then produces new offspring of the archive, and an archive selection mechanism of the external archive selects and stores the optimal solutions in the whole detection process. An experiment is performed using a real-world in-vehicle network data set to verify the performance of our proposed model and algorithm. Experimental results obtained demonstrate that our algorithm can respond quickly to attacks and achieve high entropy and detection accuracy as well as very low false positive rate with a good trade-off in the conflicting objective landscape.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Many-Objective Optimization Based Intrusion Detection for in-Vehicle Network Security


    Contributors:

    Published in:

    Publication date :

    2023-12-01


    Size :

    8991896 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Many-objective optimization based on sub-objective evolutionary algorithm

    Lei, Yuyao / Jiang, Wenzhi / Liu, Lijia et al. | British Library Online Contents | 2015


    Vehicle intrusion detection system and vehicle intrusion detection method

    KYUNG TAE HO | European Patent Office | 2017

    Free access

    VEHICLE INTRUSION DETECTION SYSTEM AND VEHICLE INTRUSION DETECTION METHOD

    KYUNG TAE HO | European Patent Office | 2018

    Free access


    Research on CAN Network Security Aspects and Intrusion Detection Design

    Li, Fang / Wang, Lifang / Wu, Yan | SAE Technical Papers | 2017