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LSTM guided homomorphic encryption for threat-resistant IoT networks

Sanjeev Kumar, Sukhvinder Singh Deora, Tajinder Kumar, Purushottam Sharma, Cheng Cheng Orcid Logo, Vishal Garg

Discover Computing, Volume: 28, Issue: 1

Swansea University Author: Cheng Cheng Orcid Logo

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DOI (Published version): 10.1007/s10791-025-09843-4

Abstract

The rapid growth of the Internet of Things (IoT) has led to revolutionary innovations in many fields; however, it has also resulted in significant security and privacy issues due to the resource limitations and distributed nature of IoT networks. Traditional cryptographic techniques or machine learn...

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Published in: Discover Computing
ISSN: 2948-2992
Published: Springer Science and Business Media LLC 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa71104
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spelling 2026-01-07T17:07:09.6095821 v2 71104 2025-12-08 LSTM guided homomorphic encryption for threat-resistant IoT networks 11ddf61c123b99e59b00fa1479367582 0000-0003-0371-9646 Cheng Cheng Cheng Cheng true false 2025-12-08 MACS The rapid growth of the Internet of Things (IoT) has led to revolutionary innovations in many fields; however, it has also resulted in significant security and privacy issues due to the resource limitations and distributed nature of IoT networks. Traditional cryptographic techniques or machine learning-based anomaly detection systems do not jointly provide data privacy and resilience to threats in real time. The existing methods, such as Homomorphic Encryption (HE), offer a high computation cost for performing encryption. Furthermore, Long Short-Term Memory (LSTM) networks can predict an anomaly profile instead of performing encryption. To address these shortcomings, this paper proposes NeuroCrypt. This new hybrid system combines Fully Homomorphic Encryption (FHE) with LSTM-based encrypted anomaly detection and supplements it with blockchain-based dynamic key management and multi-factor authentication. The architecture targets edge and fog computing settings using, among other techniques, ciphertext packing, model quantisation, and parallelised encrypted operations. The performance of the proposed framework has been evaluated on a real dataset. The results show that the accuracy in the proposed framework is 99.2% compared to existing techniques such as HE-based DNN, FL-based models, and LSTM IDS. Conclusively, NeuroCrypt provides a privacy-preserving, effective, and scalable solution to real-time threat abatement in IoT networks. Journal Article Discover Computing 28 1 Springer Science and Business Media LLC 2948-2992 Internet of things (IoT), Homomorphic encryption (HE), Long short-term memory (LSTM), Anomaly detection, Privacy-preserving computation, Blockchain, Dynamic key management, Multi-factor authentication (MFA) 9 12 2025 2025-12-09 10.1007/s10791-025-09843-4 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University SU Library paid the OA fee (TA Institutional Deal) The authors have been funded by UKRI Grant EP/W020408/1 and Grant RS718 through Doctoral Training Centre at Swansea University. 2026-01-07T17:07:09.6095821 2025-12-08T10:21:09.6198014 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Sanjeev Kumar 1 Sukhvinder Singh Deora 2 Tajinder Kumar 3 Purushottam Sharma 4 Cheng Cheng 0000-0003-0371-9646 5 Vishal Garg 6 71104__35914__d3c9df8b7eec4a749612a552100405af.pdf 71104.VoR.pdf 2026-01-07T17:05:19.7507643 Output 4008156 application/pdf Version of Record true © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License. true eng http://creativecommons.org/licenses/by/4.0/
title LSTM guided homomorphic encryption for threat-resistant IoT networks
spellingShingle LSTM guided homomorphic encryption for threat-resistant IoT networks
Cheng Cheng
title_short LSTM guided homomorphic encryption for threat-resistant IoT networks
title_full LSTM guided homomorphic encryption for threat-resistant IoT networks
title_fullStr LSTM guided homomorphic encryption for threat-resistant IoT networks
title_full_unstemmed LSTM guided homomorphic encryption for threat-resistant IoT networks
title_sort LSTM guided homomorphic encryption for threat-resistant IoT networks
author_id_str_mv 11ddf61c123b99e59b00fa1479367582
author_id_fullname_str_mv 11ddf61c123b99e59b00fa1479367582_***_Cheng Cheng
author Cheng Cheng
author2 Sanjeev Kumar
Sukhvinder Singh Deora
Tajinder Kumar
Purushottam Sharma
Cheng Cheng
Vishal Garg
format Journal article
container_title Discover Computing
container_volume 28
container_issue 1
publishDate 2025
institution Swansea University
issn 2948-2992
doi_str_mv 10.1007/s10791-025-09843-4
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
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description The rapid growth of the Internet of Things (IoT) has led to revolutionary innovations in many fields; however, it has also resulted in significant security and privacy issues due to the resource limitations and distributed nature of IoT networks. Traditional cryptographic techniques or machine learning-based anomaly detection systems do not jointly provide data privacy and resilience to threats in real time. The existing methods, such as Homomorphic Encryption (HE), offer a high computation cost for performing encryption. Furthermore, Long Short-Term Memory (LSTM) networks can predict an anomaly profile instead of performing encryption. To address these shortcomings, this paper proposes NeuroCrypt. This new hybrid system combines Fully Homomorphic Encryption (FHE) with LSTM-based encrypted anomaly detection and supplements it with blockchain-based dynamic key management and multi-factor authentication. The architecture targets edge and fog computing settings using, among other techniques, ciphertext packing, model quantisation, and parallelised encrypted operations. The performance of the proposed framework has been evaluated on a real dataset. The results show that the accuracy in the proposed framework is 99.2% compared to existing techniques such as HE-based DNN, FL-based models, and LSTM IDS. Conclusively, NeuroCrypt provides a privacy-preserving, effective, and scalable solution to real-time threat abatement in IoT networks.
published_date 2025-12-09T05:33:13Z
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score 11.096068

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