# Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection

arXiv: https://arxiv.org/abs/2512.15503 · HTML: https://mlsanity.com/paper/2512.15503/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 85 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://mlsanity.com/paper/2110.02642/) (2021)
- [TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2201.07284/) (2022)
- [Machine Learning at the Network Edge: A Survey](https://mlsanity.com/paper/1908.00080/) (2019)
- [AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Scheme](https://mlsanity.com/paper/2305.04468/) (2023)
- [Mobile Edge Intelligence and Computing for the Internet of Vehicles](https://mlsanity.com/paper/1906.00400/) (2019)
- [Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles](https://mlsanity.com/paper/2210.15496/) (2022)
- [VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs](https://mlsanity.com/paper/1804.06701/) (2018)
- [A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks](https://mlsanity.com/paper/2201.10500/) (2022)
- [Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles](https://mlsanity.com/paper/2007.00302/) (2020)
- [Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems](https://mlsanity.com/paper/1610.06810/) (2016)
- [SECMACE: Scalable and Robust Identity and Credential Management Infrastructure in Vehicular Communication Systems](https://mlsanity.com/paper/1707.05518/) (2017)
- [Edge AI-based Radio Frequency Fingerprinting for IoT Networks](https://mlsanity.com/paper/2412.10553/) (2024)
- [AttentionGuard: Transformer-based Misbehavior Detection for Secure Vehicular Platoons](https://mlsanity.com/paper/2505.10273/) (2025)
- [Scalable & Resilient Vehicle-Centric Certificate Revocation List Distribution in Vehicular Communication Systems](https://mlsanity.com/paper/2004.03407/) (2020)
- [Analyzing Attacks on Cooperative Adaptive Cruise Control (CACC)](https://mlsanity.com/paper/1710.05789/) (2017)

## Built on by (most cited first, 1 of 1)

- [PAMPOS: Causal Transformer-based Trajectory Prediction for Attack-Agnostic Misbehavior Detection in V2X Networks](https://mlsanity.com/paper/2605.06833/) (2026)

## Abstract (arXiv)

Vehicular platooning promises transformative improvements in transportation efficiency and safety through the coordination of multi-vehicle formations enabled by Vehicle-to-Everything (V2X) communication. However, the distributed nature of platoon coordination creates security vulnerabilities, allowing authenticated vehicles to inject falsified kinematic data, compromise operational stability, and pose a threat to passenger safety. Traditional misbehaviour detection approaches, which rely on plausibility checks and statistical methods, suffer from high False Positive (FP) rates and cannot capture the complex temporal dependencies inherent in multi-vehicle coordination dynamics. We present Attention In Motion (AIMformer), a transformer-based framework specifically tailored for real-time misbehaviour detection in vehicular platoons with edge deployment capabilities. AIMformer leverages multi-head self-attention mechanisms to capture intra-vehicle temporal dynamics, with a spatio-temporal variant that further models inter-vehicle spatial correlations. It incorporates global positional encoding with vehicle-specific temporal offsets to handle join/exit maneuvers. We propose a Precision-Focused Binary Cross-Entropy (PFBCE) loss function that penalizes FPs to meet the requirements of safety-critical vehicular systems. Extensive evaluation across 4 platoon controllers, multiple attack vectors, and diverse mobility scenarios demonstrates superior performance (≥ 0.93) compared to state-of-the-art baseline architectures. A comprehensive deployment analysis utilizing TensorFlow Lite (TFLite), Open Neural Network Exchange (ONNX), and TensorRT achieves sub-millisecond inference latency, making it suitable for real-time operation on resource-constrained edge platforms. Hence, validating AIMformer is viable for both in-vehicle and roadside deployment.