Modern cars include a huge number of sensors and actuators, which continuously exchange data and control commands. The most used protocol for communication of different components in automotive system is the Controller Area Network (CAN). According to CAN, components communicate by broadcasting messages on a bus. In addition, the standard definition of the protocol does not provide information for authentication, so exposing it to attacks. This paper proposes a method based on deep learning aiming at discovering attacks towards the CAN-bus. In particular, Neural Networks and MultiLayer Perceptrons are the class of networks employed in our approach. We also validate our approach by analysing a real-world dataset with the injection of messages from different types of attacks: denial of service, fuzzy pattern attacks, and attacks against specific components. The obtained results are encouraging and demonstrate the effectiveness of the approach.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    CAN-Bus Attack Detection With Deep Learning




    Publication date :

    2021-08-01


    Size :

    1959350 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Deep Learning Based Cyber Attack Detection in 6G Wireless Networks

    Gupta, Brij B. / Chui, Kwok Tai / Gaurav, Akshat et al. | IEEE | 2023


    Crossfire Attack Detection Using Deep Learning in Software Defined ITS Networks

    Narayanadoss, Akash Raj / Truong-Huu, Tram / Mohan, Purnima Murali et al. | IEEE | 2019


    Multi-Modal Deep Learning for Vehicle Sensor Data Abstraction and Attack Detection

    Rofail, Mark / Alsafty, Aysha / Matousek, Matthias et al. | IEEE | 2019


    Deep Learning-based Intra-slice Attack Detection for 5G-V2X Sliced Networks

    Boualouache, Abdelwahab / Djaidja, Taki Eddine Toufik / Senouci, Sidi-Mohammed et al. | IEEE | 2022


    Deep adversarial attack on target detection systems

    Osahor, Uche M. / Nasrabadi, Nasser M. | SPIE | 2019