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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) International Journal of Advances in Applied Sciences
Imane Laassar
Ibn Tofail University
Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Intrusion detection systems for internet of thing based big data: a review Imane Laassar; Moulay Youssef Hadi
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i1.pp87-96

Abstract

Network security is one of the foremost anxieties of the modern time. Over the previous years, numerous studies have been accompanied on the intrusion detection system. However, network security is one of the foremost apprehensions of the modern era this is due to the speedy development and substantial usage of altered technologies over the past period. The vulnerabilities of these technologies security have become a main dispute intrusion detection system is used to classify unapproved access and unusual attacks over the secured networks. For the implementation of intrusion detection system different approaches are used machine learning technique is one of them. In order to comprehend the present station of application of machine learning techniques for solving the intrusion discovery anomalies in internet of thing (IoT) based big data this review paper conducted. Total 55 papers are summarized from 2010 and 2021 which were centering on the manner of the single, hybrid and collaborative classifier design. This review paper also includes some of the basic information like IoT, big data, and machine learning approaches are discussed.
Artificial intelligence-based cloud-internet of things resource management for energy conservation Soukaina Ouhame; Moulay Youssef Hadi; Amine Mrhari; Imane Laassar
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 Amine Mrhari Moulay Youssef Hadi Soukaina Ouhame