LATIDIA · Ciberseguridad

Fuera de sincronía, fuera de la vista: ataques de estado fantasma contra la detección de intrusiones IIoT

arXiv: 2610.02552v1Tipo de anuncio: nuevo Resumen: Los sistemas de detección de intrusiones (IDS) basados en el aprendizaje automático son fundamentales para proteger los entornos del Internet Industrial de las Cosas (IIoT). La mayoría de las investigaciones contradictorias en su contra

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arXiv:2610.02552v1 Announce Type: new Abstract: Machine learning-based intrusion detection systems (IDS) are critical for securing Industrial Internet of Things (IIoT) environments. Most adversarial research against them perturbs the feature vector or the traffic that produces it, and depends on gradient access, repeated model queries, or a learned model of benign traffic. A smaller line of work reshapes packet timing without querying the detector, but makes malicious traffic mimic a learned model of benign timing. Across these approaches, one assumption of industrial monitoring pipelines has received little attention: temporal synchronization. An IDS reconstructs operational state by aggregating telemetry into sliding or tumbling windows, so its view depends not only on what