INDRA: Intrusion Detection using Recurrent Autoencoders in Automotive Embedded Systems
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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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jul 2020.
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Citing papers per year: 2021: 4; 2022: 2; 2023: 2; 2024: 1; 2025: 1; 2026: 2.
12 papers.
- 2021Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directionscited by 22
- 2021LATTE: LSTM Self-Attention based Anomaly Detection in Embedded Automotive Platformscited by 9
- 2022CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Levelcited by 8
- 2021TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systemscited by 6
- 2022Roadmap for Cybersecurity in Autonomous Vehiclescited by 5
- 2023Machine Learning Accelerators in 2.5D Chiplet Platforms with Silicon Photonicscited by 5
- 2023Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computingcited by 3
- 2024A Survey of Anomaly Detection in In-Vehicle Networkscited by 1
- 2021Detecting message modification attacks on the CAN bus with Temporal Convolutional Networkscited by 0
- 2025A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Networkcited by 0
- 2026Autonomous Systems Dependability in the era of AI: Design Challenges in Safety, Security, Reliability and Certificationcited by 0
- 2026CAN We Trust Your Results? A Cross-Dataset Study of Automotive IDS Evaluationcited by 0
- 2026CAN We Trust Your Results? A Cross-Dataset Study of Automotive IDS Evaluation29 Jun 2026
- 2026Autonomous Systems Dependability in the era of AI: Design Challenges in Safety, Security, Reliability and Certification30 Apr 2026
- 2025A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network15 May 2025
- 2024A Survey of Anomaly Detection in In-Vehicle Networks11 Sep 2024
- 2023Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computing22 Mar 2023
- 2023Machine Learning Accelerators in 2.5D Chiplet Platforms with Silicon Photonics28 Jan 2023
- 2022CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Level3 May 2022
- 2022Roadmap for Cybersecurity in Autonomous Vehicles19 Jan 2022
- 2021TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems9 Sep 2021
- 2021LATTE: LSTM Self-Attention based Anomaly Detection in Embedded Automotive Platforms12 Jul 2021
- 2021Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions16 Jun 2021
- 2021Detecting message modification attacks on the CAN bus with Temporal Convolutional Networks16 Jun 2021