INDRA: Intrusion Detection using Recurrent Autoencoders in Automotive Embedded Systems
This addresses security vulnerabilities in automotive embedded systems, which is critical for vehicle safety but appears to be an incremental improvement over existing methods.
The authors tackled the problem of detecting security attacks in connected vehicles by developing INDRA, a novel intrusion detection system using GRU-based recurrent autoencoders for CAN bus anomaly detection, which they evaluated under various attack scenarios and compared against prior works.
Today's vehicles are complex distributed embedded systems that are increasingly being connected to various external systems. Unfortunately, this increased connectivity makes the vehicles vulnerable to security attacks that can be catastrophic. In this work, we present a novel Intrusion Detection System (IDS) called INDRA that utilizes a Gated Recurrent Unit (GRU) based recurrent autoencoder to detect anomalies in Controller Area Network (CAN) bus-based automotive embedded systems. We evaluate our proposed framework under different attack scenarios and also compare it with the best known prior works in this area.