# AttentionGuard: Transformer-based Misbehavior Detection for Secure Vehicular Platoons

arXiv: https://arxiv.org/abs/2505.10273 · HTML: https://mlsanity.com/paper/2505.10273/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 15 May 2025.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs](https://mlsanity.com/paper/1804.06701/) (2018)
- [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)

## Built on by (most cited first, 3 of 3)

- [Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection](https://mlsanity.com/paper/2512.15503/) (2025)
- [PAMPOS: Causal Transformer-based Trajectory Prediction for Attack-Agnostic Misbehavior Detection in V2X Networks](https://mlsanity.com/paper/2605.06833/) (2026)
- [SPADE: SPaT Attack Detection from the Connected Vehicle's Perspective](https://mlsanity.com/paper/2609.02741/) (2026)

## Abstract (arXiv)

Vehicle platooning, with vehicles traveling in close formation coordinated through Vehicle-to-Everything (V2X) communications, offers significant benefits in fuel efficiency and road utilization. However, it is vulnerable to sophisticated falsification attacks by authenticated insiders that can destabilize the formation and potentially cause catastrophic collisions. This paper addresses this challenge: misbehavior detection in vehicle platooning systems. We present AttentionGuard, a transformer-based framework for misbehavior detection that leverages the self-attention mechanism to identify anomalous patterns in mobility data. Our proposal employs a multi-head transformer-encoder to process sequential kinematic information, enabling effective differentiation between normal mobility patterns and falsification attacks across diverse platooning scenarios, including steady-state (no-maneuver) operation, join, and exit maneuvers. Our evaluation uses an extensive simulation dataset featuring various attack vectors (constant, gradual, and combined falsifications) and operational parameters (controller types, vehicle speeds, and attacker positions). Experimental results demonstrate that AttentionGuard achieves up to 0.95 F1-score in attack detection, with robust performance maintained during complex maneuvers. Notably, our system performs effectively with minimal latency (100ms decision intervals), making it suitable for real-time transportation safety applications. Comparative analysis reveals superior detection capabilities and establishes the transformer-encoder as a promising approach for securing Cooperative Intelligent Transport Systems (C-ITS) against sophisticated insider threats.