Out of Sync, Out of Sight: Phantom State Attacks against IIoT Intrusion Detection

2026-10-01

AI Summary

This study addresses the vulnerability of Industrial Internet of Things (IIoT) intrusion detection systems that rely on time-window aggregation to temporal synchronization attacks. We propose the Phantom State Attack (PSA), which injects bounded temporal drifts to push observations toward window boundaries, thereby inducing erroneous state reconstruction. Operating in a zero-query passive mode, PSA requires neither packet tampering nor model fitting, exploiting solely the vulnerabilities inherent in temporal aggregation. Analytical evaluations under both sliding and tumbling window configurations, along with empirical assessments on the ToN-IoT and CIC datasets, demonstrate that PSA significantly degrades detection rates for targeted traffic. Compared to baseline methods, the proposed attack achieves superior efficiency and stealthiness, revealing a novel attack surface for IIoT security.

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 is observed but on when each observation falls relative to a window boundary. We introduce the Phantom State Attack (PSA), which exploits that dependence under a passive, zero-query threat model. Rather than modifying packets, perturbing features, querying the classifier, or fitting any model of benign traffic, PSA injects bounded timing drift calibrated to the attack flow's own inter-arrival variability, moving observations across the nearest window boundary by the minimal shift needed. The IDS then reconstructs a phantom state that diverges from the true process state. We evaluate PSA on ToN-IoT and CIC IIoT 2025 (DataSense), against Random Forest, MLP and XGBoost, measuring detection degradation, synchronization distortion, stealth, and attacker cost. PSA degrades detection on flows carrying enough packets for window-boundary redistribution, and leaves others almost unchanged, so its effect is conditional. A query-based baseline reaches higher raw success but needs many queries per window, while PSA needs none. The results identify temporal aggregation as an attack surface reachable under weaker assumptions than prior evasion techniques.

Problem

• Industrial Internet of Things
• Intrusion Detection Systems
• Adversarial Attacks
• Temporal Synchronization
• Phantom State Attack

Innovation

• Phantom State Attack
• Intrusion Detection System
• Temporal Synchronization
• Zero-query Evasion
• Industrial Internet of Things