{"id":25760,"date":"2026-09-03T12:37:50","date_gmt":"2026-09-03T12:37:50","guid":{"rendered":"https:\/\/scientificassociation.org\/?post_type=journal-paper&#038;p=25760"},"modified":"2026-09-05T07:51:56","modified_gmt":"2026-09-05T07:51:56","slug":"machine-learning-based-quality-of-service-monitoring-and-anomaly-detection-for-mobile-telecommunications-networks","status":"publish","type":"journal-paper","link":"https:\/\/scientificassociation.org\/ar\/journal-paper\/machine-learning-based-quality-of-service-monitoring-and-anomaly-detection-for-mobile-telecommunications-networks\/","title":{"rendered":"Machine Learning-Based Quality of Service Monitoring and Anomaly Detection for Mobile Telecommunications Networks"},"content":{"rendered":"<div class=\"padding_abstract justify ltr\">Mobile telecommunication networks frequently experience Quality of Service (QoS) degradation and anoma-lous network conditions that traditional rule-based monitoring systems may struggle to detect. This study in-vestigates the effectiveness of machine learning techniques for accurate, reliable, and efficient QoS monitor-ing and anomaly detection using the CSE-CIC-IDS2018 dataset. The proposed system, termed NetSentinel, consists of two integrated components. The first component performs QoS monitoring by generating a binary quality label (&#8220;Good&#8221; or &#8220;Degraded&#8221;) based on four key QoS metrics: throughput, latency, jitter, and packet loss. The second component performs anomaly detection by classifying network traffic as either &#8220;Normal&#8221; or &#8220;Anomaly&#8221; using 72 selected network flow features and five domain-driven engineered features. Six machine learning classifiers were evaluated: Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, and K-Nearest Neighbours. For QoS monitoring, Decision Tree and Gradient Boost-ing achieved perfect performance, attaining an accuracy and F1-score of 1.00, while Random Forest achieved 99% across all evaluation metrics. For anomaly detection, Decision Tree and Random Forest achieved near-perfect results with an accuracy of 1.00 and an F1-score of 99%, while Logistic Regression produced an F1-score of 97%, demonstrating its effectiveness as a baseline model. The trained models were deployed in a web-based application built using a FastAPI backend and JavaScript frontend, supporting live packet capture, manual input, and real-time visualization. Experimental results demonstrate that tree-based machine learning models provide the best balance of accuracy, robustness, interpretability, and deployment suitability for intel-ligent network monitoring in mobile telecommunications environments.<\/div>\n","protected":false},"featured_media":25762,"template":"","meta":{"_acf_changed":false},"journal-name":[220],"paper-tag":[232,278,276],"class_list":["post-25760","journal-paper","type-journal-paper","status-publish","has-post-thumbnail","hentry","journal-name-jaiep","paper-tag-issue-2","paper-tag-november-2026","paper-tag-volume-3"],"acf":[],"_links":{"self":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-paper\/25760","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-paper"}],"about":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/types\/journal-paper"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/media\/25762"}],"wp:attachment":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/media?parent=25760"}],"wp:term":[{"taxonomy":"journal-name","embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-name?post=25760"},{"taxonomy":"paper-tag","embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/paper-tag?post=25760"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}