  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.
