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.


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    Titel :

    Stacked LSTM Based Anomaly Detection in Time-Critical Automotive Networks


    Beteiligte:


    Erscheinungsdatum :

    2023-09-02


    Format / Umfang :

    32 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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