{"id":4184,"date":"2025-11-12T10:09:47","date_gmt":"2025-11-12T09:09:47","guid":{"rendered":"https:\/\/meta-os.eu\/?p=4184"},"modified":"2025-11-12T12:32:57","modified_gmt":"2025-11-12T11:32:57","slug":"securing-networks-designing-and-evaluating-a-machine-learning-system-for-anomaly-detection","status":"publish","type":"post","link":"https:\/\/meta-os.eu\/index.php\/2025\/11\/12\/securing-networks-designing-and-evaluating-a-machine-learning-system-for-anomaly-detection\/","title":{"rendered":"Securing Networks: Designing and Evaluating a Machine Learning System for Anomaly Detection"},"content":{"rendered":"\n<div style=\"height:39px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Background: In the last decade, numerous methods have been proposed to define and detect outliers, particularly in complex environments like networks, where anomalies significantly deviate from normal patterns. Although defining a clear standard is challenging, anomaly detection systems have become essential for network administrators to efficiently identify and resolve irregularities. Methods: This study develops and evaluates a machine learning-based system for network anomaly detection, focusing on point anomalies within network traffic. It employs both unsupervised and supervised learning techniques, including change point detection, clustering, and classification models, to identify anomalies.<\/p>\n\n\n\n<p>SHAP values are utilized to enhance model interpretability. Results: Unsupervised models effectively captured temporal patterns, while supervised models, particularly Random Forest (94.3%), demonstrated high accuracy in classifying anomalies, closely approximating the actual anomaly rate. Conclusions: Experimental results indicate that the system can accurately predict network anomalies in advance. Congestion and packet loss were identified as key factors in anomaly detection. This study demonstrates the potential for real-world deployment of the anomaly detection system to validate its scalability.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.mdpi.com\/2673-2688\/5\/4\/143\">https:\/\/www.mdpi.com\/2673-2688\/5\/4\/143<\/a><\/p>\n\n\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Background: In the last decade, numerous methods have been proposed to define and detect outliers, particularly in complex environments like networks, where anomalies significantly deviate from normal patterns. Although defining a clear standard is challenging, anomaly detection systems have become &hellip;<\/p>\n","protected":false},"author":4,"featured_media":4188,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_mi_skip_tracking":false,"footnotes":""},"categories":[17],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v17.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Securing Networks: Designing and Evaluating a Machine Learning System for Anomaly Detection - META-OS<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/meta-os.eu\/index.php\/2025\/11\/12\/securing-networks-designing-and-evaluating-a-machine-learning-system-for-anomaly-detection\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Securing Networks: Designing and Evaluating a Machine Learning System for Anomaly Detection - META-OS\" \/>\n<meta property=\"og:description\" content=\"Background: In the last decade, numerous methods have been proposed to define and detect outliers, particularly in complex environments like networks, where anomalies significantly deviate from normal patterns. Although defining a clear standard is challenging, anomaly detection systems have become &hellip;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/meta-os.eu\/index.php\/2025\/11\/12\/securing-networks-designing-and-evaluating-a-machine-learning-system-for-anomaly-detection\/\" \/>\n<meta property=\"og:site_name\" content=\"META-OS\" \/>\n<meta property=\"article:published_time\" content=\"2025-11-12T09:09:47+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-11-12T11:32:57+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/meta-os.eu\/wp-content\/uploads\/2025\/11\/SHsquare.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1299\" \/>\n\t<meta property=\"og:image:height\" content=\"1299\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Michalis Karadimos\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebSite\",\"@id\":\"https:\/\/meta-os.eu\/#website\",\"url\":\"https:\/\/meta-os.eu\/\",\"name\":\"IOT NGIN\",\"description\":\"\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/meta-os.eu\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":\"ImageObject\",\"@id\":\"https:\/\/meta-os.eu\/index.php\/2025\/11\/12\/securing-networks-designing-and-evaluating-a-machine-learning-system-for-anomaly-detection\/#primaryimage\",\"inLanguage\":\"en-US\",\"url\":\"https:\/\/meta-os.eu\/wp-content\/uploads\/2025\/11\/SHsquare.png\",\"contentUrl\":\"https:\/\/meta-os.eu\/wp-content\/uploads\/2025\/11\/SHsquare.png\",\"width\":1299,\"height\":1299},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/meta-os.eu\/index.php\/2025\/11\/12\/securing-networks-designing-and-evaluating-a-machine-learning-system-for-anomaly-detection\/#webpage\",\"url\":\"https:\/\/meta-os.eu\/index.php\/2025\/11\/12\/securing-networks-designing-and-evaluating-a-machine-learning-system-for-anomaly-detection\/\",\"name\":\"Securing Networks: Designing and Evaluating a Machine Learning System for Anomaly Detection - 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