ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Computer Security
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1622382
This article is part of the Research TopicCyber Resilience in IoE: Integrating Artificial Intelligence for Robust SecurityView all 4 articles
Smart Framework for Industrial IoT & Cloud Computing Network Intrusion Detection Using a ConvLSTM-Based Deep Learning Model
Provisionally accepted- 1Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
- 2Department of Informatics and Computer Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia
- 3University of Hafr Al Batin, Hafar Al Batin, Saudi Arabia
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
In the rapidly evolving landscape of the Industrial Internet of Things (IIoT) and cloud computing, ensuring robust network security has become a major challenge for the Internet of Everything (IoE). However, this technological advancement has also introduced new vulnerabilities, making these systems prime targets for sophisticated cyberattacks. Ensuring the security of IIoT and cloud networks is critical to protecting sensitive data and maintaining industrial operations' integrity. This study examines data anonymity, security, and preservation in the Edge IIoT environment, focusing on cloud computing and cyber-physical systems. The integration of blockchain in industrial applications introduces additional security risks. This paper uses the EdgeIIoT dataset, enriched with security threat detection features for blockchain environments. The ConvLSTM framework, which uses the characteristics of two deep neural network models, CNN and LSTM, predicts and mitigates threats in IoT, IIoT, and cloud environments. The ConvLSTM model shows outstanding results for accuracy, precision, recall, and F1 score on multiple datasets based on network intrusion detection, showcasing its robustness and generalizability. The results are compared with previously published research work in this domain to demonstrate the superiority of the proposed framework.
Keywords: Internet of Everything (IoE), Industrial Internet of Thing, Cloud computing, Network intrusion detection, artificial intelligence, ConvLSTM, neural networks
Received: 03 May 2025; Accepted: 16 Jul 2025.
Copyright: © 2025 Eshmawi, Aldrees and Alharthi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Raed Alharthi, University of Hafr Al Batin, Hafar Al Batin, Saudi Arabia
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.