Evidence map›Paper›PMID 41381788›Full record

ArticleScientific reports2025

Quantum-aware secure blockchain intrusion detection system for industrial IoT networks.

Nasir Hussain, Shuaiyong Li, Altaf Hussain, Zahid Ullah, Mona Jamjoom

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Nasir HussainSchool of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Shuaiyong LiKey Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Altaf HussainSchool of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China. altafkfm74@gmail.com.
Zahid UllahInformation Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia.
Mona JamjoomDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia.

Funding

Princess Nourah bint Abdulrahman University Researchers Supporting Project PNURSP2025R104
6 · The paper itself

Abstract

The Industrial Internet of Things (IIoT) integrates sensors, actuators, controllers, and gateways across critical industrial sectors such as manufacturing, energy, oil and gas, and transportation. The convergence of operational technology (OT) and Information Technology (IT) has enabled real-time automation, predictive maintenance, and large-scale process optimization. However, IIoT infrastructures are increasingly exposed to sophisticated cyberattacks, ranging from ransomware and Advanced Persistent Threats (APTs) to false data injection and denial-of-service (DoS) campaigns. Moreover, the emergence of quantum computing introduces an additional dimension of vulnerability, since widely deployed public-key cryptographic schemes such as RSA and elliptic-curve cryptography (ECC) will be rendered insecure under Shor's algorithm, while Grover's algorithm accelerates brute-force search against symmetric ciphers. In anticipation of this threat, the security of IIoT must evolve toward post-quantum resilience. This paper proposes a Quantum-Aware Secure Blockchain Intrusion Detection System (QASB-IDS), a novel hybrid framework that combines intrusion detection with post-quantum blockchain security and quantum key distribution (QKD). The system introduces a hierarchical consortium blockchain tailored to IIoT, ensuring tamper-proof and decentralized storage of intrusion detection alerts and anomaly model updates. The IDS employs a lightweight hybrid signature-anomaly detection approach that extracts features from IIoT traffic and classifies them using a CNN-LSTM model optimized for constrained devices using Edge-IIoTset, SWaT, and XIIoTID datasets. Model updates are securely aggregated with privacy guarantees and anchored on blockchain using lattice-based post-quantum signatures. Critical gateways additionally deploy QKD to protect session key distribution. Evaluation using recent industrial intrusion datasets demonstrates that QASB-IDS provides improved detection accuracy, low false alarms, and strong resilience against both classical and quantum-capable adversaries.

Indexed as

IIoT cyber securityIndustrial IoTIntrusion detection systemQASB-IDSQuantum key distributionQuantum optimization

Identifiers

PMID41381788
PMCPMC12816754

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.