# LATTE: LSTM Self-Attention based Anomaly Detection in Embedded Automotive Platforms

arXiv: https://arxiv.org/abs/2107.05561 · HTML: https://mlsanity.com/paper/2107.05561/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data](https://mlsanity.com/paper/1906.02492/) (2019)
- [Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension](https://mlsanity.com/paper/2010.06396/) (2020)
- [Time Series Forecasting Using LSTM Networks: A Symbolic Approach](https://mlsanity.com/paper/2003.05672/) (2020)
- [INDRA: Intrusion Detection using Recurrent Autoencoders in Automotive Embedded Systems](https://mlsanity.com/paper/2007.08795/) (2020)
- [Securing Vehicle-to-Everything (V2X) Communication Platforms](https://mlsanity.com/paper/2003.07191/) (2020)
- [Public Plug-in Electric Vehicles + Grid Data: Is a New Cyberattack Vector Viable?](https://mlsanity.com/paper/1907.08283/) (2019)

## Built on by (most cited first, 9 of 9)

- [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)
- [Robust Perception Architecture Design for Automotive Cyber-Physical Systems](https://mlsanity.com/paper/2205.08067/) (2022)
- [R-TOSS: A Framework for Real-Time Object Detection using Semi-Structured Pruning](https://mlsanity.com/paper/2303.02191/) (2023)
- [A Survey of Anomaly Detection in In-Vehicle Networks](https://mlsanity.com/paper/2409.07505/) (2024)
- [Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directions](https://mlsanity.com/paper/2407.21184/) (2024)
- [UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles](https://mlsanity.com/paper/2501.04213/) (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 can be thought of as complex distributed embedded systems that run a variety of automotive applications with real-time constraints. Recent advances in the automotive industry towards greater autonomy are driving vehicles to be increasingly connected with various external systems (e.g., roadside beacons, other vehicles), which makes emerging vehicles highly vulnerable to cyber-attacks. Additionally, the increased complexity of automotive applications and the in-vehicle networks results in poor attack visibility, which makes detecting such attacks particularly challenging in automotive systems. In this work, we present a novel anomaly detection framework called LATTE to detect cyber-attacks in Controller Area Network (CAN) based networks within automotive platforms. Our proposed LATTE framework uses a stacked Long Short Term Memory (LSTM) predictor network with novel attention mechanisms to learn the normal operating behavior at design time. Subsequently, a novel detection scheme (also trained at design time) is used to detect various cyber-attacks (as anomalies) at runtime. We evaluate our proposed LATTE framework under different automotive attack scenarios and present a detailed comparison with the best-known prior works in this area, to demonstrate the potential of our approach.