Today’s vehicles are increasingly connected with various external systems (e.g., roadside beacons, and other vehicles) to meet the goals of autonomy, which makes them highly vulnerable to multiple cyber-attacks. Moreover, the increased complexity of automotive applications and the in-vehicle networks resulted in poor attack visibility, which makes detecting such attacks particularly challenging in automotive systems. In this chapter, we present a novel anomaly detection framework called LATTE to detect cyber-attacks in Controller Area Network (CAN) bus based automotive systems. Our proposed LATTE framework uses a stacked Long Short Term Memory (LSTM) predictor network with a novel attention mechanism to learn the normal operating behavior at design time. At runtime, LATTE employs a novel detection scheme (also trained at design time) to detect various cyber-attacks (as anomalies). Moreover, we evaluate our proposed LATTE framework under different automotive attack scenarios and present a detailed comparison with the best-known prior works in this area to demonstrate the effectiveness of our approach.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Stacked LSTM Based Anomaly Detection in Time-Critical Automotive Networks




    Publication date :

    2023-09-02


    Size :

    32 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





    Automotive network switch with anomaly detection

    RUAN XIAOFAN / YEO YEE-SENG | European Patent Office | 2020

    Free access

    Automotive network switch with anomaly detection

    RUAN XIAOFAN / YEO YEE-SENG | European Patent Office | 2022

    Free access


    CANnolo: An Anomaly Detection System based on LSTM Autoencoders for Controller Area Network

    Longari, Stefano / Valcarcel, Daniel Humberto Nova / Zago, Mattia et al. | BASE | 2021

    Free access