# INDRA: Intrusion Detection using Recurrent Autoencoders in Automotive Embedded Systems

arXiv: https://arxiv.org/abs/2007.08795 · HTML: https://mlsanity.com/paper/2007.08795/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions](https://mlsanity.com/paper/2106.09527/) (2021)
- [LATTE: LSTM Self-Attention based Anomaly Detection in Embedded Automotive Platforms](https://mlsanity.com/paper/2107.05561/) (2021)
- [CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Level](https://mlsanity.com/paper/2205.01306/) (2022)
- [TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems](https://mlsanity.com/paper/2109.04565/) (2021)
- [Roadmap for Cybersecurity in Autonomous Vehicles](https://mlsanity.com/paper/2201.10349/) (2022)
- [Machine Learning Accelerators in 2.5D Chiplet Platforms with Silicon Photonics](https://mlsanity.com/paper/2301.12252/) (2023)
- [Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computing](https://mlsanity.com/paper/2303.12910/) (2023)
- [A Survey of Anomaly Detection in In-Vehicle Networks](https://mlsanity.com/paper/2409.07505/) (2024)
- [Detecting message modification attacks on the CAN bus with Temporal Convolutional Networks](https://mlsanity.com/paper/2106.08692/) (2021)
- [A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network](https://mlsanity.com/paper/2505.11551/) (2025)
- [Autonomous Systems Dependability in the era of AI: Design Challenges in Safety, Security, Reliability and Certification](https://mlsanity.com/paper/2604.27807/) (2026)
- [CAN We Trust Your Results? A Cross-Dataset Study of Automotive IDS Evaluation](https://mlsanity.com/paper/2606.30430/) (2026)

## Abstract (arXiv)

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.