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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
FATTAH, Mohammed
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Resource placement strategy optimization for smart grid application using 5G wireless networks Chafi, Saad-Eddine; Balboul, Younes; Mazer, Said; Fattah, Mohammed; El Bekkali, Moulhime
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3932-3942

Abstract

With the evolution of 5G-network, wireless mobile networks are growing to take a strong stand in attempts to achieve ubiquitous large-scale acquisition, connectivity and processing. Smart-grids are among the critical areas that can benefit from the capabilities of the 5G-network, especially internet of things (IoT) applications such as massive machine-type-communications or ultra-reliable low-latency communications. A distributed cloud-services use the cloud, fog and edge computing infrastructures and applications to take advantage of every available resource including network equipment and connected objects to optimize cost, energy, and latency depending on the planned optimization criteria. In this article, we present smart-grid solution based on cloud-services and 5G-network, then we study the integration of smart-grid services in the cloud based on: placement in the cloud and in the end-device, and finally we introduce our proposed solution based on Intelligent placement strategy. The scenarios are evaluated by the iFogSim simulator, and the analyzed results compare the standard cloud placement, edge placement and our intelligent placement with regard to the optimization of the energy consumption, latency, and network usage. The findings show that cloud energy consumption can be substantially reduced using Intelligent Placement while respecting the potential central processing unit (CPU) processing power-limit for each IoT-device used and network constraints in smart-grid.
Hybrid software defined network-based deep learning framework for enhancing internet of medical things cybersecurity Rbah, Yahya; Mahfoudi, Mohammed; Balboul, Younes; Chetioui, Kaouthar; Fattah, Mohammed; Mazer, Said; Elbekkali, Moulhime; Bernoussi, Benaissa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3599-3610

Abstract

The risk of cyber-attacks has increased significantly with the rapid development of the Internet of Medical Things (IoMT). The proliferation of IoMT devices in healthcare facilities has made conventional intrusion detection approaches challenging to employ. Our study proposes a novel hybrid framework leveraging Software Defined Network (SDN) controllers and deep learning techniques, specifically Convolutional Neural Networks (CNN) and Bidirectional Long-Term Memory (Bi-LSTM), to address these challenges. Our framework introduces a unique combination of SDN and deep learning, allowing for dynamic and efficient management of IoMT security. The integration of CNN and Bi-LSTM enables the system to handle diverse data types encountered in IoMT, offering a comprehensive approach to threat detection. Unlike traditional methods, our hybrid solution adapts seamlessly to the evolving threat landscape of healthcare IoT systems. The urgency of our research is affirmed by the critical need to fortify IoMT security amid escalating cyber threats. The conventional methods struggle to cope with the complex nature of IoMT networks, making our exploration of a hybrid SDN-based deep learning framework imperative. With a background in cybersecurity and a dedicated focus on healthcare IoT, we recognize the urgency to develop a solution that not only enhances detection accuracy but also ensures real-time responsiveness in healthcare settings. The proposed method has been validated using the “IoT-Healthcare security” dataset, revealing its efficacy in detecting numerous frequent threats and outperforming current state-of-the-art techniques in terms of high detection accuracy of 99.97% and less than 1.8 (s) in terms of speed efficiency.
Data access control for named data of health things EL-Bakkouchi, Asmaa; EL Ghazi, Mohammed; Bouayad, Anas; Fattah, Mohammed; EL Bekkali, Moulhime
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6219

Abstract

The internet of health things (IoHT) represents an innovative network concept that significantly improving healthcare. However, security and privacy are the main concerns of IoHT because the transmitted health data is often sensitive data about patients’ health status, which needs to be secured and protected from unauthorized users and any leakage. Named data networking (NDN) is considered the most promising architecture for the future internet that perfectly fits with the requirements of IoHT systems, especially regarding security and privacy. In this paper, we exploit the fundamental features of NDN to design a robust system for IoHT to ensure secure communication and access to health data. This system presents a content access control model, which prevents attackers and unauthorized users from accessing health data, allows only authorized users to access these data, and prevents users from accessing “corrupted” or “fake” content. The simulation results show that the proposed mechanism slightly delays the secure retrieval of health data. However, this delay is tolerable since the mechanism protects the health data from unauthorized persons and those who try to inject untrusted data into the network.
Co-Authors Balboul, Younes Bernoussi, Benaissa Bouayad, Anas Chafi, Saad-Eddine Chetioui, Kaouthar EL BEKKALI, Moulhime El Ghazi, Mohammed EL-Bakkouchi, Asmaa Elbekkali, Moulhime Mahfoudi, Mohammed MAZER, Said Rbah, Yahya