{
  "abstract": "Integrated sensing and communication (ISAC) enables wireless infrastructure to jointly support communication and environmental sensing, but also introduces new physical-layer security threats. A malicious reconfigurable intelligent surface (RIS) can manipulate its time-varying reflection phases to generate spoofing echoes and distort the sensing results of a roadside unit (RSU). Existing trajectory-level detectors rely on reconstructed sensing parameters and vehicle trajectories, making their performance dependent on upstream estimation accuracy. In this paper, we propose a signal-level Green Learning (GL) framework that directly detects RIS spoofing from matched-filtered RSU echoes. The proposed method combines two-stage Subspace Approximation with Adjusted Bias (SAAB), Discriminant Feature Test (DFT), and eXtreme Gradient Boosting (XGBoost) for spatial-temporal representation learning, feature selection, and classification, respectively. Simulation results show 99.335\\% detection accuracy for a 32-element malicious RIS at $-80$ dBm receiver noise power. GL remains competitive with Long Short-Term Memory (LSTM) and bidirectional LSTM (BiLSTM) baselines while requiring only 0.1119 million floating-point operations (FLOPs), and also generalizes to previously unseen RIS sizes.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.01881",
  "author_total": 6,
  "authors": [
    {
      "name": "Po-Heng Chou"
    },
    {
      "name": "Tingyu Shui"
    },
    {
      "name": "Pin-Qi Fu"
    },
    {
      "name": "Lien-Sheng Chang"
    },
    {
      "name": "Walid Saad"
    },
    {
      "name": "Li-Chun Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cs4sbpsa2ja8p.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.01881",
  "fulltext_url": null,
  "id": "cs4sbpsa2ja8p",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.01881",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "eess.SP"
    ],
    [
      "arxiv",
      "math.IT"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.01881",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cs4sbpsa2ja8p.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:10Z",
      "native_id": "2611.01881",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.01881"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "Green Learning-Based Signal-Level Detection of RIS Spoofing Attacks in Vehicular ISAC Systems",
  "title_text": "Green Learning-Based Signal-Level Detection of RIS Spoofing Attacks in Vehicular ISAC Systems",
  "type": "preprint",
  "updated_at": "2026-11-04T15:21:05Z",
  "url": "https://research.draconitic.com/paper/cs4sbpsa2ja8p",
  "venue": "arXiv",
  "versions": []
}