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  "path": "/news/2026-05-graph-frequency-anomaly-framework-accuracy.html",
  "publishedAt": "2026-05-28T20:40:02.000Z",
  "site": "https://techxplore.com",
  "tags": [
    "Engineering"
  ],
  "textContent": "A new machine learning framework designed to detect malicious interference in unmanned aerial vehicles (UAVs), commonly known as drones, has shown strong performance in identifying both sudden and slow-developing sensor attacks, according to research in the International Journal of Automation and Control. The system, called GTF-MAD (Graph Time-Frequency Mixed Anomaly Detection), achieved a peak F1 score of 99.71% in detecting bias in tests on a quadrotor drone.",
  "title": "Graph Time-Frequency Mixed Anomaly Detection framework achieves 99.71% accuracy detecting sensor attacks on drones"
}