eprintid: 15504 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/01/55/04 datestamp: 2024-12-04 23:30:08 lastmod: 2024-12-04 23:30:10 status_changed: 2024-12-04 23:30:08 type: article metadata_visibility: show creators_name: Aldribi, Abdulaziz creators_name: Singh, Aman creators_name: Breñosa, Jose creators_id: creators_id: aman.singh@uneatlantico.es creators_id: josemanuel.brenosa@uneatlantico.es title: Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica full_text_status: public keywords: Internet of Things (IoT); Edge of Things (EoT); fog computing; cloud computing; scalable; decentralized abstract: Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications. Internet of Things (IoT), fog computing, edge computing, cloud computing, and the edge of things are the spine of all real-time and scalable applications. Conspicuously, this study proposed a novel framework for a real-time and scalable application that changes dynamically with time. In this study, IoT deployment is recommended for data acquisition. The Pre-Processing of data with local edge and fog nodes is implemented in this study. The threshold-oriented data classification method is deployed to improve the intrusion detection mechanism’s performance. The employment of machine learning-empowered intelligent algorithms in a distributed manner is implemented to enhance the overall response rate of the layered framework. The placement of respondent nodes near the framework’s IoT layer minimizes the network’s latency. For economic evaluation of the proposed framework with minimal efforts, EdgeCloudSim and FogNetSim++ simulation environments are deployed in this study. The experimental results confirm the robustness of the proposed system by its improvised threshold-oriented data classification and intrusion detection approach, improved response rate, and prediction mechanism. Moreover, the proposed layered framework provides a robust solution for real-time and scalable applications that changes dynamically with time. date: 2023-04 publication: Computer Systems Science and Engineering volume: 46 number: 3 pagerange: 3865-3881 id_number: doi:10.32604/csse.2023.037748 refereed: TRUE issn: 0267-6192 official_url: http://doi.org/10.32604/csse.2023.037748 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Artículos y libros Universidad Internacional Iberoamericana México > Investigación > Producción Científica Abierto Inglés Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications. Internet of Things (IoT), fog computing, edge computing, cloud computing, and the edge of things are the spine of all real-time and scalable applications. Conspicuously, this study proposed a novel framework for a real-time and scalable application that changes dynamically with time. In this study, IoT deployment is recommended for data acquisition. The Pre-Processing of data with local edge and fog nodes is implemented in this study. The threshold-oriented data classification method is deployed to improve the intrusion detection mechanism’s performance. The employment of machine learning-empowered intelligent algorithms in a distributed manner is implemented to enhance the overall response rate of the layered framework. The placement of respondent nodes near the framework’s IoT layer minimizes the network’s latency. For economic evaluation of the proposed framework with minimal efforts, EdgeCloudSim and FogNetSim++ simulation environments are deployed in this study. The experimental results confirm the robustness of the proposed system by its improvised threshold-oriented data classification and intrusion detection approach, improved response rate, and prediction mechanism. Moreover, the proposed layered framework provides a robust solution for real-time and scalable applications that changes dynamically with time. metadata Aldribi, Abdulaziz; Singh, Aman y Breñosa, Jose mail SIN ESPECIFICAR, aman.singh@uneatlantico.es, josemanuel.brenosa@uneatlantico.es (2023) Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications. Computer Systems Science and Engineering, 46 (3). pp. 3865-3881. ISSN 0267-6192 document_url: http://repositorio.uneatlantico.es/id/eprint/15504/1/TSP_CSSE_37748.pdf