{"id":12565,"date":"2026-09-28T14:36:35","date_gmt":"2026-09-28T12:36:35","guid":{"rendered":"https:\/\/computaex.es\/publicaciones\/artificial-intelligence-for-intrusion-detection-through-side-channel-techniques\/"},"modified":"2026-09-28T14:36:35","modified_gmt":"2026-09-28T12:36:35","slug":"artificial-intelligence-for-intrusion-detection-through-side-channel-techniques","status":"publish","type":"page","link":"https:\/\/computaex.es\/en\/publicaciones\/artificial-intelligence-for-intrusion-detection-through-side-channel-techniques\/","title":{"rendered":"Artificial Intelligence for Intrusion Detection Through Side-Channel Techniques"},"content":{"rendered":"<p><strong>Abstract:<\/strong><\/p>\n<p>The rapid expansion of Internet of Things (IoT) technologies has introduced diverse applications while simultaneously exposing devices to increasing cybersecurity risks.Sensitive data handled within IoT networks and the limited resources of connected devices make conventional intrusion detection methods often impractical.This work introduces an approach for detecting cyberattacks in IoT environments through side-channel analysis based on device power consumption.A lightweight machine learning framework is employed to identify anomalous behavior without disrupting normal device operation.Experiments conducted on various setups, including custom datasets and unseen attack patterns, confirm the system&#039;s effectiveness and real-time detection capability.The proposed solution stands out for its simplicity, reproducibility, and ease of deployment across heterogeneous IoT infrastructures with minimal computational overhead.<\/p>\n<p><strong>Autores:<\/strong> Felipe Lemus-Prieto, Jos\u00e9\u2010Luis Gonz\u00e1lez\u2010S\u00e1nchez, Andres Caro<\/p>\n<p><a href=\"https:\/\/doi.org\/10.3390\/engproc2026123018\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.3390\/engproc2026123018<\/a><\/p>","protected":false},"excerpt":{"rendered":"<p>The rapid expansion of Internet of Things (IoT) technologies has introduced diverse applications while simultaneously exposing devices to increasing cybersecurity risks.Sensitive data handled within IoT networks and the limited resources of connected devices make conventional intrusion detection methods often impractical.This work introduces an approach for detecting cyberattacks in IoT environments through side-channel analysis based on device power consumption.A lightweight machine learning framework is employed to identify anomalous behavior without disrupting normal device operation.Experiments conducted on various setups, including custom datasets\u2026<\/p>","protected":false},"author":1,"featured_media":0,"parent":1583,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[123,91],"tags":[],"class_list":["post-12565","page","type-page","status-publish","hentry","category-2026-publicaciones","category-publicaciones"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.5 - 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