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

• URL: http://arxiv.org/abs/2606.22939v1
• Date: Mon, 22 Jun 2026 07:16:50 GMT
• Title: CITADEL: CSI-Based Jamming Detection and Open-Set Classification for IIoT Networks
• Abstract summary: Radio frequency jamming poses a critical threat to the availability of wireless Industrial Internet of Things (IIoT) networks.<n>Existing detection and classification techniques are poorly suited to this setting.<n>This paper presents a lightweight two-stage hierarchical pipeline that uses only Channel State Information (CSI) measurements.
• Score: 8.234024185555208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Detecting Backdoors in Object Detection via Pre-NMS Prediction Distribution Shift [35.70747932100589]
  DistScan is a backdoor detection framework based on a simple but previously unexploited observation.<n>It substantially outperforms existing methods, improving average detection accuracy over the best-performing applicable baseline by 27.32 percentage points.
  arXiv  Detail & Related papers  (2026-08-19T16:38:39Z)
• Deep Learning-Driven Friendly Jamming for Secure Multicarrier ISAC Under Channel Uncertainty [36.06255760148067]
  Integrated sensing and communication (ISAC) systems promise efficient spectrum utilization by jointly supporting radar sensing and wireless communication.<n>This paper presents a deep learning-driven framework for enhancing physical-layer security in multicarrier ISAC systems under imperfect channel state information (CSI) and in the presence of unknown eavesdropper (Eve) locations.
  arXiv  Detail & Related papers  (2026-03-05T11:20:27Z)
• Neurosymbolic Learning for Advanced Persistent Threat Detection under Extreme Class Imbalance [29.991707658663188]
  This paper proposes a neurosymbolic architecture that integrates an optimized BERT model with logic tensor networks (LTN) for explainable APT detection in wireless IoT networks.<n>Results show that neurosymbolic learning enables high-performance, interpretable, and operationally viable APT detection for IoT network monitoring architectures.
  arXiv  Detail & Related papers  (2026-02-28T04:25:50Z)
• MI$^2$DAS: A Multi-Layer Intrusion Detection Framework with Incremental Learning for Securing Industrial IoT Networks [47.386868423451595]
  MI$2$DAS is a multi-layer intrusion detection framework that integrates anomaly-based hierarchical traffic pooling and open-set recognition.<n>Experiments conducted on the Edge-IIoTset dataset demonstrate strong performance across all layers.<n>These results showcase MI$2$DAS as an effective, scalable and adaptive framework for enhancing IIoT security.
  arXiv  Detail & Related papers  (2026-02-27T09:37:05Z)
• HubScan: Detecting Hubness Poisoning in Retrieval-Augmented Generation Systems [0.031068858355080494]
  hubness can be exploited to introduce harmful content, alter search rankings, bypass content filtering, and decrease system performance.<n>We introduce hubscan, an open-source security scanner that evaluates vector indices and embeddings to identify hubs in RAG systems.<n> hubscan achieves 90% recall at a 0.2% alert budget and 100% recall at 0.4%, with adversarial hubs ranking above the 99.8th percentile.
  arXiv  Detail & Related papers  (2026-02-25T21:37:53Z)
• Decentralized Multi-Agent Swarms for Autonomous Grid Security in Industrial IoT: A Consensus-based Approach [0.0]
  DMAS agents communicate via a lightweight peer-to-peer protocol to cooperatively detect anomalous behavior.<n>Agents vote on the threat level of an identified threat, enabling instant quarantine of a compromised node or nodes.<n> DMAS demonstrated sub-millisecond response times, 97.3% accuracy in detecting malicious activity under high load, and 87% accuracy in detecting zero-day attacks.
  arXiv  Detail & Related papers  (2026-01-24T04:25:36Z)
• Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security [6.649910168731417]
  Edge computing-based Next-Generation Wireless Networks (NGWN)-IoT offer enhanced bandwidth capacity for large-scale service provisioning.<n>Existing intrusion detection and prevention methods provide limited security as adversaries continually adapt their attack strategies.<n>We propose a dynamic attack detection and prevention approach to address this challenge.
  arXiv  Detail & Related papers  (2025-04-22T19:36:19Z)
• A Robust Multi-Stage Intrusion Detection System for In-Vehicle Network Security using Hierarchical Federated Learning [0.0]
  In-vehicle intrusion detection systems (IDSs) must detect seen attacks and provide a robust defense against new, unseen attacks. Previous work has relied solely on the CAN ID feature or has used traditional machine learning (ML) approaches with manual feature extraction. This paper introduces a cutting-edge, novel, lightweight, in-vehicle, IDS-leveraging, deep learning (DL) algorithm to address these limitations.
  arXiv  Detail & Related papers  (2024-08-15T21:51:56Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• CSI: Enhancing the Robustness of 3D Point Cloud Recognition against Corruption [33.70232326721406]
  Real-world safety-critical applications present challenges due to unavoidable data corruption. In this study, we harness the inherent set property of point cloud data to introduce a novel critical subset identification (CSI) method. Our CSI framework integrates two pivotal components: density-aware sampling (DAS) and self-entropy minimization (SEM)
  arXiv  Detail & Related papers  (2023-10-05T07:30:52Z)
• Locate and Verify: A Two-Stream Network for Improved Deepfake Detection [33.50963446256726]
  Current deepfake detection methods are typically inadequate in generalizability. We propose an innovative two-stream network that effectively enlarges the potential regions from which the model extracts evidence. We also propose a Semi-supervised Patch Similarity Learning strategy to estimate patch-level forged location annotations.
  arXiv  Detail & Related papers  (2023-09-20T08:25:19Z)
• Uncertainty-Aware Deep Calibrated Salient Object Detection [74.58153220370527]
  Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. These methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem. We introduce an uncertaintyaware deep SOD network, and propose two strategies to prevent deep SOD networks from being overconfident.
  arXiv  Detail & Related papers  (2020-12-10T23:28:36Z)
• Multi-stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks [17.2528983535773]
  This research focuses on deploying a multi-stage machine learning-based intrusion detection (ML-IDS) in 5G C-RAN. It can detect and classify four types of jamming attacks: constant jamming, random jamming, jamming, and reactive jamming. The final classification accuracy of attacks is 94.51% with a 7.84% false negative rate.
  arXiv  Detail & Related papers  (2020-04-13T17:21:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.