CITADEL: CSI-Based Jamming Detection and Open-Set Classification for IIoT Networks

2026-06-22

AI Summary

This work addresses the vulnerability of wireless Industrial Internet of Things (IIoT) systems to radio-frequency interference attacks, for which existing detection methods suffer from limitations in information utilization, scalability, or identification of unknown attacks. The authors propose CITADEL, a lightweight, two-stage hierarchical detection framework that leverages only native channel state information (CSI) from commercial off-the-shelf IIoT devices, establishing the first end-to-end CSI-based interference detection system. By integrating open-set classification with adversarial training, CITADEL enables real-time inference on edge GPUs while simultaneously achieving closed-set classification of known attacks, open-set identification of zero-day interference, and robustness against adversarial evasion. Experimental results demonstrate 100% detection accuracy for six known attack types, 97.1% detection rate across fifteen zero-day scenarios, a false positive rate of merely 0.4%, adversarial evasion success below 5%, and low latency of 14.2 ms per inference with energy consumption of 95.9 mJ.

Abstract

Radio frequency jamming poses a critical threat to the availability of wireless Industrial Internet of Things (IIoT) networks. Existing detection and classification techniques are poorly suited to this setting: coarse signal-strength and cross-layer features lack information richness, while raw I/Q baseband approaches require hardware and throughput that is impractical at the scale of hundred-node IIoT deployments. This paper presents CITADEL, a lightweight two-stage hierarchical pipeline that uses only Channel State Information (CSI) measurements, which are natively available on commodity IIoT devices, to detect and classify jamming attacks including previously unseen ones. While prior work has shown that jamming leaves observable CSI signatures, CITADEL is the first system to translate this insight into an end-to-end pipeline that jointly achieves closed-set classification of known attacks, open-set detection of zero-day attacks, and resistance to adversarial evasion. Evaluated across 6 known attack types and 15 zero-day scenarios, CITADEL achieves 100% known-attack detection and 97.1% zero-day detection at a 0.4% end-to-end false positive rate. Under adversarial evaluation spanning white-box and black-box threat models, gradient-based evasion remains below 2% across all tested perturbation budgets and the strongest published CSI attack generator achieves less than 5% average evasion. A systematic comparison against eight baselines confirms that no existing method achieves comparable performance on CSI data across all three axes: detection, generalization, and robustness. The full pipeline completes inference in 14.2 ms at 95.9 mJ on an edge GPU, establishing CITADEL as a practical solution for large-scale IIoT network security.

Problem

• jamming detection
• Industrial Internet of Things
• Channel State Information
• open-set classification
• wireless security

Innovation

• CSI-based jamming detection
• open-set classification
• adversarial robustness
• lightweight IIoT security
• zero-day attack detection