# TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems

arXiv: https://arxiv.org/abs/2109.04565 · HTML: https://mlsanity.com/paper/2109.04565/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [DARTS: Deceiving Autonomous Cars with Toxic Signs](https://mlsanity.com/paper/1802.06430/) (2018)
- [CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data](https://mlsanity.com/paper/1906.02492/) (2019)
- [INDRA: Intrusion Detection using Recurrent Autoencoders in Automotive Embedded Systems](https://mlsanity.com/paper/2007.08795/) (2020)
- [LATTE: LSTM Self-Attention based Anomaly Detection in Embedded Automotive Platforms](https://mlsanity.com/paper/2107.05561/) (2021)
- [Viden: Attacker Identification on In-Vehicle Networks](https://mlsanity.com/paper/1708.08414/) (2017)

## Built on by (most cited first, 6 of 6)

- [CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Level](https://mlsanity.com/paper/2205.01306/) (2022)
- [Roadmap for Cybersecurity in Autonomous Vehicles](https://mlsanity.com/paper/2201.10349/) (2022)
- [A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels](https://mlsanity.com/paper/2209.06828/) (2022)
- [Robust Perception Architecture Design for Automotive Cyber-Physical Systems](https://mlsanity.com/paper/2205.08067/) (2022)
- [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)

## Abstract (arXiv)

Modern vehicles have multiple electronic control units (ECUs) that are connected together as part of a complex distributed cyber-physical system (CPS). The ever-increasing communication between ECUs and external electronic systems has made these vehicles particularly susceptible to a variety of cyber-attacks. In this work, we present a novel anomaly detection framework called TENET to detect anomalies induced by cyber-attacks on vehicles. TENET uses temporal convolutional neural networks with an integrated attention mechanism to detect anomalous attack patterns. TENET is able to achieve an improvement of 32.70% in False Negative Rate, 19.14% in the Mathews Correlation Coefficient, and 17.25% in the ROC-AUC metric, with 94.62% fewer model parameters, 86.95% decrease in memory footprint, and 48.14% lower inference time when compared to the best performing prior work on automotive anomaly detection.