  Modern vehicles can be thought of as complex distributed embedded systems
that run a variety of automotive applications with real-time constraints.
Recent advances in the automotive industry towards greater autonomy are driving
vehicles to be increasingly connected with various external systems (e.g.,
roadside beacons, other vehicles), which makes emerging vehicles highly
vulnerable to cyber-attacks. Additionally, the increased complexity of
automotive applications and the in-vehicle networks results in poor attack
visibility, which makes detecting such attacks particularly challenging in
automotive systems. In this work, we present a novel anomaly detection
framework called LATTE to detect cyber-attacks in Controller Area Network (CAN)
based networks within automotive platforms. Our proposed LATTE framework uses a
stacked Long Short Term Memory (LSTM) predictor network with novel attention
mechanisms to learn the normal operating behavior at design time. Subsequently,
a novel detection scheme (also trained at design time) is used to detect
various cyber-attacks (as anomalies) at runtime. 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
potential of our approach.
