PAMPOS: Causal Transformer-based Trajectory Prediction for Attack-Agnostic Misbehavior Detection in V2X Networks

• URL: http://arxiv.org/abs/2605.06833v1
• Date: Thu, 07 May 2026 18:38:15 GMT
• Title: PAMPOS: Causal Transformer-based Trajectory Prediction for Attack-Agnostic Misbehavior Detection in V2X Networks
• Abstract summary: We present PAMPOS, a causal transformer-decoder trained on benign VeReMi++ trajectories to learn normal mobility patterns.<n>At inference time, misbehavior is identified as a deviation from the model's next-step kinematic predictions.<n>We evaluate PAMPOS across all 19 attack types in VeReMi++ under rush-hour and afternoon scenarios.
• Score: 0.764671395172401
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems [6.42094170804706]
  adversarial attacks add carefully crafted perturbations to network traffic data that leads to misclassifications.<n>This paper asks what type of architectures actually hold up when attackers try to manipulate the systems.<n>For practitioners deploying intrusion detection in adversarial environments, we recommend CNN-based architectures.
  arXiv  Detail & Related papers  (2026-06-10T13:41:44Z)
• Downstream Transfer Attack: Adversarial Attacks on Downstream Models with Pre-trained Vision Transformers [95.22517830759193]
  This paper studies the transferability of such an adversarial vulnerability from a pre-trained ViT model to downstream tasks. We show that DTA achieves an average attack success rate (ASR) exceeding 90%, surpassing existing methods by a huge margin.
  arXiv  Detail & Related papers  (2024-08-03T08:07:03Z)
• FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning [98.43475653490219]
  Federated learning (FL) is susceptible to poisoning attacks. FreqFed is a novel aggregation mechanism that transforms the model updates into the frequency domain. We demonstrate that FreqFed can mitigate poisoning attacks effectively with a negligible impact on the utility of the aggregated model.
  arXiv  Detail & Related papers  (2023-12-07T16:56:24Z)
• On Trace of PGD-Like Adversarial Attacks [77.75152218980605]
  Adversarial attacks pose safety and security concerns for deep learning applications. We construct Adrial Response Characteristics (ARC) features to reflect the model's gradient consistency. Our method is intuitive, light-weighted, non-intrusive, and data-undemanding.
  arXiv  Detail & Related papers  (2022-05-19T14:26:50Z)
• DAD: Data-free Adversarial Defense at Test Time [21.741026088202126]
  Deep models are highly susceptible to adversarial attacks. Privacy has become an important concern, restricting access to only trained models but not the training data. We propose a completely novel problem of 'test-time adversarial defense in absence of training data and even their statistics'
  arXiv  Detail & Related papers  (2022-04-04T15:16:13Z)
• AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural Networks [18.461079157949698]
  backdoor poisoning attacks against image classification networks. We propose lightweight automated detection and correction techniques against poisoning attacks. Our technique outperforms existing defenses such as NeuralCleanse and STRIP on popular benchmarks.
  arXiv  Detail & Related papers  (2022-01-31T23:42:32Z)
• Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios [22.22337220509128]
  Backdoor attacks (BAs) are an emerging threat to deep neural network classifiers. We propose a detection framework based on BP reverse-engineering and a novel it expected transferability (ET) statistic.
  arXiv  Detail & Related papers  (2022-01-20T22:21:38Z)
• Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings [40.332149464256496]
  Adrial robustness of deep models is pivotal in ensuring safe deployment in real world settings. We propose a self-supervised method to detect adversarial attacks and classify them to their respective threat models. We use a SimCLR encoder in our experiments, since we show the SimCLR embedding distance is a good proxy for human perceptibility.
  arXiv  Detail & Related papers  (2021-08-30T16:39:52Z)
• Anomaly Detection in Cybersecurity: Unsupervised, Graph-Based and Supervised Learning Methods in Adversarial Environments [63.942632088208505]
  Inherent to today's operating environment is the practice of adversarial machine learning. In this work, we examine the feasibility of unsupervised learning and graph-based methods for anomaly detection. We incorporate a realistic adversarial training mechanism when training our supervised models to enable strong classification performance in adversarial environments.
  arXiv  Detail & Related papers  (2021-05-14T10:05:10Z)
• Anomaly Detection-Based Unknown Face Presentation Attack Detection [74.4918294453537]
  Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection. In this paper, we present a deep-learning solution for anomaly detection-based spoof attack detection. The proposed approach benefits from the representation learning power of the CNNs and learns better features for fPAD task.
  arXiv  Detail & Related papers  (2020-07-11T21:20:55Z)
• Detection as Regression: Certified Object Detection by Median Smoothing [50.89591634725045]
  This work is motivated by recent progress on certified classification by randomized smoothing. We obtain the first model-agnostic, training-free, and certified defense for object detection against $ell$-bounded attacks.
  arXiv  Detail & Related papers  (2020-07-07T18:40:19Z)
• Adversarial Detection and Correction by Matching Prediction Distributions [0.0]
  The detector almost completely neutralises powerful attacks like Carlini-Wagner or SLIDE on MNIST and Fashion-MNIST. We show that our method is still able to detect the adversarial examples in the case of a white-box attack where the attacker has full knowledge of both the model and the defence.
  arXiv  Detail & Related papers  (2020-02-21T15:45:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.