{"id":5158,"date":"2026-04-21T11:40:36","date_gmt":"2026-04-21T11:40:36","guid":{"rendered":"https:\/\/cognitionconferences.com\/cybersecurity\/?post_type=speaker&#038;p=5158"},"modified":"2026-04-21T11:55:34","modified_gmt":"2026-04-21T11:55:34","slug":"lukasz-apiecionek","status":"publish","type":"speaker","link":"https:\/\/cognitionconferences.com\/cybersecurity\/speaker\/lukasz-apiecionek\/","title":{"rendered":"Lukasz Apiecionek"},"content":{"rendered":"<p><strong>Absract<br \/>\n<\/strong><\/p>\n<p>The Fuzzy Deep Neural Network for Cybersecurity in Edge\/IoT systems (FDNN) is a novel deep neural network architecture developed by incorporating fuzzy numbers into traditional artificial neural network structures. This approach enables the development of new cybersecurity solutions for anomaly detection, intrusion detection, and cyberattack identification. Current research results confirm the effectiveness and quality of the proposed solution. Experimental evaluations conducted so far demonstrate that fuzzy neural networks can achieve data prediction performance comparable to traditional neural networks. However, the key advantage of fuzzy networks is their significantly lower computational power and RAM requirements. This was achieved through the implementation of ordered fuzzy numbers in the computational processes performed by the artificial neural network. This approach increases the amount of information stored within neurons while maintaining very simple arithmetic operations. Furthermore, the studies revealed a significant increase in training speed compared to existing frameworks. As a result, the solution can be effectively deployed on edge devices and IoT systems, which typically have substantially lower computational capabilities than data centers. The proposed algorithms enable local computation directly on edge devices, eliminating the need to transmit data to centralized computing centers, thereby reducing latency, data transmission costs, and energy consumption. Additionally, the implementation of fuzzy numbers in convolutional neural networks enabled image recognition performance comparable to traditional networks, while requiring less complex network architectures.<\/p>\n<p><strong>\u00a0<\/strong><\/p>\n","protected":false},"featured_media":5103,"template":"","meta":{"_acf_changed":false},"schedule":[5],"speaker-category":[6],"class_list":["post-5158","speaker","type-speaker","status-publish","has-post-thumbnail","hentry","schedule-day-1","speaker-category-speakers"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Lukasz Apiecionek | Kazimierz Wielki University | Poland<\/title>\n<meta name=\"description\" content=\"\u0141ukasz Apiecionek has completed his PhD at the age of 32 years from Institute of Fundamental Technological Research POLISH ACADEMY OF SCIENCE. He is the director of Cybersecurity Cathedra on Faculty of Computer Science at Kazimierz Wielki Univesrity in Bydgoszcz, Poland. He has published more than 80 papers in reputed journals and conferences.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cognitionconferences.com\/cybersecurity\/speaker\/lukasz-apiecionek\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Lukasz Apiecionek | Kazimierz Wielki University | Poland\" \/>\n<meta property=\"og:description\" content=\"\u0141ukasz Apiecionek has completed his PhD at the age of 32 years from Institute of Fundamental Technological Research POLISH ACADEMY OF SCIENCE. He is the director of Cybersecurity Cathedra on Faculty of Computer Science at Kazimierz Wielki Univesrity in Bydgoszcz, Poland. 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