"Leveraging AI Knowledge Distillation for Deployable Cybersecurity Defense Systems by Mahdi Rabbani" CIC N.B.

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Leveraging AI Knowledge Distillation for Deployable Cybersecurity Defense Systems by Mahdi Rabbani

Canadian Institute for Cybersecurity (CIC)

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49 views Feb 6, 2026 #CybersecurityAwareness #Cybersecurity #AIResearch

This webinar is part of the Cyber Pulse by CIC series hosted by Sumit Kundu. Deep neural networks have demonstrated strong performance on complex cybersecurity tasks; however, large model sizes and reliance on extensive labeled datasets limit their suitability for real-time deployment. Knowledge Distillation (KD) addresses these limitations by transferring knowledge from a high-capacity teacher model to a lightweight student model. The process employs temperature-scaled soft logits that capture dark knowledge, revealing inter-class relationships beyond conventional one-hot labels. From a theoretical perspective, KD maximizes the mutual information shared between teacher and student representations, thereby enhancing generalization. This webinar presents both a theoretical overview of the student-teacher learning framework and a practical demonstration of how KD enhances the efficiency and effectiveness of lightweight models in cybersecurity applications. Dr. Mahdi Rabbani is a Research Scientist at the Canadian Institute for Cybersecurity, University of New Brunswick. He holds a Ph.D. in Computer Science and Technology from Nanjing University of Science and Technology, where he specialized in machine learning approaches for detecting malicious behavior in intrusion detection systems. He also earned an M.Tech. in Artificial Intelligence and a B.E. in Software Engineering. Dr. Rabbani has led and contributed to several industry-driven R&D projects with partners such as MasterCard Canada and IBM. His recent work includes developing frameworks for detecting credential stuffing, phishing attacks, and IoT anomalies—leveraging graph learning, explainable AI, and knowledge distillation. With a robust academic and research background, he specializes in cybersecurity, machine learning, and behavioral threat detection, focusing on scalable and interpretable solutions to address emerging cyber threats. He has published extensively in top-tier journals and conferences in the cybersecurity domain. -------------------------------

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