AttentionGuard: Transformer-based Misbehavior Detection for Secure Vehicular Platoons

TL;DR

AttentionGuard introduces a transformer-based framework for detecting misbehavior in vehicular platoons, effectively identifying sophisticated falsification attacks with high accuracy and low latency, enhancing the security of C-ITS systems.

Contribution

The paper presents a novel transformer-encoder approach for misbehavior detection in vehicle platooning, demonstrating superior detection performance over existing methods.

Findings

Achieves up to 0.95 F1-score in attack detection.

Maintains robust performance during complex maneuvers.

Operates effectively with minimal latency (100ms decision interval).

Abstract

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.