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telecommunication network using machine learning technique  \nZhunussov Ayan1, Baikenov Alimzhan1, Manankova Olga2, Zheltayev Timur1, Ziyekenov Toktalyk1  \n1Department of Telecommunications and Space Engineering, Faculty of Telecommunications and Innovation Technologies, Almaty University of Power Engineering and Telecommunications, Almaty, Kazakhstan 2Department of Cybersecurity, Faculty of Computer Sciences and Cybersecurity, International University of Information Technology,  \nAlmaty, Kazakhstan  \nArticle history:  \nReceived Oct 20, 2022 Revised Jun 18, 2023 Accepted Aug 2, 2023  \nKeywords:  \nMaсhine learning Monitoring PADT  \nPPPoE  \nQuality of service management Virtual local area network  \nCorresponding Author:  \nDesigning and implementing a fail-safe, real-time telecommunications network is complex. In modern networks, traditional quality of service (QoS) methods for monitoring and analyzing data have some problems, such as accuracy and efficient processing of big data in real time. To solve this problem, should use an appropriate intelligent crash classification system to detect and diagnose runtime errors. The article proposes to use a comprehensive fault detection system that includes QoS and machine learning technologies using information about the state of a point-to-point protocol over ethernet (PPPoE) session on PPPoE active discovery termination (PADT) virtual local area network (VLAN) routes. This intelligent system is built using the machine learning method and is independent of the main real-time system. Demonstrated the operation of seven machine learning algorithms and presented the results of training and fault detection. Based on the received information about the state of the PPPoE session, the PADT type allows you to control the behavior of the real-time system.  \nThis is an open access article under the CC BY-SA license.  \nManankova Olga  \nDepartment of Cybersecurity, Faculty of Computer Sciences and Cybersecurity International University of Information Technology  \nAlmaty, Kazakhstan  \nEmail: [olga.manank@gmail.com](olga.manank@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIn modern communication networks, effective troubleshooting plays a pivotal role in ensuring optimal service quality for end users. Internet service providers (ISPs) grapple with the challenge of delivering highquality services to customers while upholding network stability and dependability. Detecting and rectifying network failures is a significant hurdle faced by ISPs [1] . Consequently, a robust fault detection system becomes indispensable to quality of service (QoS) and uphold customer satisfaction.  \nA notable issue in network communication pertains to the occurrence of session failures within pointto-point protocol over ethernet (PPPoE) sessions [2],[3]. Detecting failures in PPPoE sessions poses challenges since this operates at the data link layer, responsible for transmitting and receiving data packets between nodes. However, this layer lacks mechanisms to verify packet delivery to their intended destinations. In response, this article proposes an all-encompassing fault detection system that melds QoS and machine learning technologies. This system scrutinizes the status of PPPoE sessions across virtual local area network (VLAN) routes, utilizing real-time PPPoE active discovery termination (PADT) data from network devices. Its role is to detect, diagnose, and address network failures, thus providing actionable insights to network administrators.  \nExisting research on detecting network communication failures has chiefly centered on QoS mechanisms that prioritize traffic based on its importance and utilization [4], [5] . More recently, machine-  \nlearning algorithms have emerged as tools for detecting and prognosticating network failures. These algorithms learn from historical traffic data, proficiently predicting future traffic patterns to allocate resources effectively and shape t","cbCaijbYrruUo7Lu","https://ap.wps.com/l/cbCaijbYrruUo7Lu","pdf",512728,"English","# Article Info ABSTRACT\n# 1. INTRODUCTION","[{\"question\":\"What problem does the article target in telecommunication QoS management?\",\"answer\":\"It targets limitations of traditional QoS methods for accurate and efficient real-time monitoring and analysis, especially when processing big data.\"},{\"question\":\"How does the proposed system detect and diagnose faults?\",\"answer\":\"It integrates QoS with machine learning and analyzes PPPoE session status over VLAN routes using real-time PADT data from network devices.\"},{\"question\":\"Which resources does the system use to support real-time control of the main system?\",\"answer\":\"It uses information about the PPPoE session state, where the detected PADT type is used to control the behavior of the real-time system.\"}]","Quality of service management in telecommunication network using machine learning technique - Paper | PDF",23]