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All Journal International Journal of Advances in Applied Sciences
Hadi, Moulay Youssef
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Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Proposed algorithm base optimization scheme for intrusion detection using feature selection Laassar, Imane; Hadi, Moulay Youssef
International Journal of Advances in Applied Sciences Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i1.pp24-32

Abstract

The number of devices linked to the internet is rapidly increasing as the internet has become ingrained in every aspect of modern life. However, certain issues are getting worse, and their resolutions are not well-defined. One of the main issues is convergence and speed for communication between different internet of things (IoT) devices and their security. For that purpose, in this paper, an improved artificial bee colony (ABC) algorithm with binary search equations along with neural networks is proposed, known as the artificial bee colony algorithm with binary search equations (BABCN) algorithm for intrusion detection in terms of convergence and speed for communication. The depth-first search framework and binary search equations on which the artificial bee colony algorithm with binary search equations algorithm is built improve the algorithm’s capacity for exploitation and speed up convergence. The initial weight and threshold value of the ABC neural networks are optimized using an algorithm to prevent them from entering a local optimum during the training procedure and accelerating training. The NSL-KDD dataset was used, and based on the results; the proposed algorithm improves classification and has high intrusion detection ability in the network. The proposed has undergone tests to be evaluated, and the results show that it performs better in detection accuracy, time, and false positive rate.
Artificial intelligence-based cloud-internet of things resource management for energy conservation Ouhame, Soukaina; Hadi, Moulay Youssef; Mrhari, Amine; Laassar, Imane
International Journal of Advances in Applied Sciences 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/ijaas.v13.i3.pp507-514

Abstract

The widespread demand for hosting application services in the cloud has been fueled by the deployment of cloud data centers (CDCs) on a global scale. Furthermore, modern apps' resource needs have sharply increased, especially in industries that use a lot of data. As a result, more cloud servers have been made available, resulting in higher energy usage and, ecological problems. Large-scale data centers have been developed as a result of the rapidly increasing demand for cloud services, allowing application service providers to rent data center space for application deployment by user-required quality of service (QoS). These data centers use a lot of electricity, which raises running expenses and produces more carbon dioxide (CO2) emissions. Modern cloud computing environments must also provide QoS for their users, necessitating a trade-off between power performance, energy consumption, and service-level agreement (SLA) compliance. We present an intelligent resource management policy using enforcement learning for CDCs. The objective is to continuously consolidate and dynamically allocate virtual machines (VMs). Utilizing live migration and disabling inactive nodes to reduce power consumption in this cloud environment while maintaining service quality. To enable dynamic resource management, a better power-performance tradeoff, and significantly lower energy consumption, we integrate several artificial intelligence concepts. Based on the result the proposed approach is more efficient as compared with other techniques.
Co-Authors Laassar, Imane Mrhari, Amine Ouhame, Soukaina