用因果Transformer检测车联网异常行为，无需攻击标签也能发现未知攻击。
PAMPOS: Causal Transformer-based Trajectory Prediction for Attack-Agnostic Misbehavior Detection in V2X Networks
- 基于正常车流轨迹学习，通过因果注意力捕捉运动规律。
- 在19种攻击下AUC最高达0.98，多数攻击F1值达0.95。
- 适合需要防御未知攻击的车联网安全系统部署。
车联网中的异常行为检测是应对内部伪造攻击的第二道防线，传统加密手段无法解决此类问题。现有基于学习的检测方案依赖带标签的攻击样本进行监督训练，难以应对未见过的伪造攻击。本文提出PAMPOS，一种基于因果Transformer解码器的模型，仅在正常VeReMi++轨迹上训练，学习标准移动模式。推理时，通过比较实际轨迹与模型预测的下一帧运动状态，利用Top-K归一化异常评分机制识别偏离，定位到具体运动特征，无需攻击标注数据。我们在VeReMi++中全部19类攻击下进行了评估，涵盖高峰时段与下午场景，结果显示最高AUC达0.98，多数攻击类别F1得分可达0.95。
原文摘要 · Abstract (English)
Misbehavior detection in Vehicle-to-Everything (V2X) networks is a second line of defense against insider falsification attacks that cryptographic mechanisms alone cannot address. Existing learning-based Misbehavior Detection Schemes (MDSs) are supervised, requiring labeled attack samples at training time, thus failing to counter unseen falsification attacks. We present PAMPOS, a causal transformer-decoder trained on benign VeReMi++ trajectories to learn normal mobility patterns. At inference time, misbehavior is identified as a deviation from the model's next-step kinematic predictions using a top-K normalized anomaly scoring mechanism that localizes falsification to specific kinematic features, without requiring attack-labeled training data. We evaluate PAMPOS across all 19 attack types in VeReMi++ under rush-hour and afternoon scenarios, achieving Area Under the Curve (AUC) values of up to 0.98 and F1-scores of up to 0.95 for most attack categories.
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