{"id":2844,"date":"2025-06-24T06:21:30","date_gmt":"2025-06-24T03:21:30","guid":{"rendered":"https:\/\/resources.today\/en\/?p=2844"},"modified":"2025-06-24T06:21:30","modified_gmt":"2025-06-24T03:21:30","slug":"01nzor225","status":"publish","type":"post","link":"https:\/\/resources.today\/en\/01nzor225.html","title":{"rendered":"Analysis and development of models for detection of technical information leakage channels using machine learning"},"content":{"rendered":"<p style=\"text-align: right;\"><strong>Chabanov Ilya Dmitrievich<\/strong><br \/>\nFar Eastern Federal University, Vladivostok, Russia<br \/>\nE-mail: Chabanov.id@dvfu.ru; slepoivareskyn@gmail.com<br \/>\nRSCI: <a href=\"https:\/\/elibrary.ru\/author_profile.asp?id=1291373\">https:\/\/elibrary.ru\/author_profile.asp?id=1291373<\/a><\/p>\n<p style=\"text-align: justify;\"><strong>Abstract.<\/strong> The article addresses the challenge of detecting technical channels of information leakage amid increasing sophistication of modern information security threats. The research relevance stems from the need to automate the detection of covert surveillance devices, particularly critical for small and medium enterprises that cannot afford expensive equipment or highly qualified specialists.<\/p>\n<p style=\"text-align: justify;\">The authors analyze existing leakage detection methods, including radio frequency monitoring, electromagnetic field analysis, and power line inspection, identifying their key limitations: expert dependency, high costs, and insufficient adaptability to hidden threats. As a solution, the paper proposes machine learning techniques capable of analyzing anomalies in RF signals without human intervention.<\/p>\n<p style=\"text-align: justify;\">The study examines four classification models in detail, each focusing on different signal parameters: frequency compliance with authorized devices; temporal activity patterns; signal periodicity; emission source localization.<\/p>\n<p style=\"text-align: justify;\">The scientific novelty lies in developing a hybrid system that integrates these models using weighted voting, significantly reducing false positives while improving detection accuracy. The practical significance involves creating a cost-effective solution compatible with existing information protection systems.<\/p>\n<p style=\"text-align: justify;\">Prospects for further research include expanding the set of analyzed features, testing in real conditions and implementation in real practices of organizing information security in enterprises.<\/p>\n<p style=\"text-align: justify;\"><strong>Keywords:<\/strong> technical channels of information leakage; machine learning; radio frequency analysis; information protection; monitoring automation; hybrid model; signal anomalies<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Chabanov Ilya Dmitrievich Far Eastern Federal University, Vladivostok, Russia E-mail: Chabanov.id@dvfu.ru; slepoivareskyn@gmail.com RSCI: https:\/\/elibrary.ru\/author_profile.asp?id=1291373 Abstract. The article addresses the challenge of detecting technical channels of information leakage amid increasing sophistication of modern information security threats. The research relevance stems from &hellip;<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[44,5],"tags":[],"class_list":["post-2844","post","type-post","status-publish","format-standard","hentry","category-issue-2-2025","category-article"],"_links":{"self":[{"href":"https:\/\/resources.today\/en\/wp-json\/wp\/v2\/posts\/2844","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/resources.today\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/resources.today\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/resources.today\/en\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/resources.today\/en\/wp-json\/wp\/v2\/comments?post=2844"}],"version-history":[{"count":1,"href":"https:\/\/resources.today\/en\/wp-json\/wp\/v2\/posts\/2844\/revisions"}],"predecessor-version":[{"id":2845,"href":"https:\/\/resources.today\/en\/wp-json\/wp\/v2\/posts\/2844\/revisions\/2845"}],"wp:attachment":[{"href":"https:\/\/resources.today\/en\/wp-json\/wp\/v2\/media?parent=2844"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/resources.today\/en\/wp-json\/wp\/v2\/categories?post=2844"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/resources.today\/en\/wp-json\/wp\/v2\/tags?post=2844"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}