Journal article 74 views 8 downloads
LSTM guided homomorphic encryption for threat-resistant IoT networks
,
Vishal Garg
Discover Computing, Volume: 28, Issue: 1
Swansea University Author:
Cheng Cheng
-
PDF | Version of Record
© The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License.
Download (3.82MB)
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...
| Published in: | Discover Computing |
|---|---|
| ISSN: | 2948-2992 |
| Published: |
Springer Science and Business Media LLC
2025
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa71104 |
| 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 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. |
|---|---|
| Keywords: |
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) |
| College: |
Faculty of Science and Engineering |
| Funders: |
The authors have been funded by UKRI Grant EP/W020408/1 and Grant RS718 through Doctoral Training Centre at Swansea University. |
| Issue: |
1 |