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.
Stacked LSTM Based Anomaly Detection in Time-Critical Automotive Networks
Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems ; Kapitel : 11 ; 349-380
2023-09-02
32 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch