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DTSTART;TZID="Pacific Time (US & Canada)":20260217T131000
DTEND;TZID="Pacific Time (US & Canada)":20260217T140000
SUMMARY:CySER Virtual Seminar – Securing Machine Learning: Evolving Threats, Attacks, and Defenses
LOCATION:Online
DESCRIPTION:Title: Securing Machine Learning: Evolving Threats, Attacks, and Defenses\n\nSpeaker: Dr. Yong (Steve) Wang\n\nAbstract: Machine learning (ML) has gained increasing attention in recent years, with applications spanning nearly every industry. However, its widespread adoption has also led to a rise in security threats. This presentation explores evolving threats, attacks, and defense strategies against adversarial attempts on ML models. Specifically, we will examine two types of adversarial attacks: exploration attacks targeting hypersphere-based ML models and exploitation attacks affecting both tree-based supervised and unsupervised ML models. Additionally, we will introduce defense mechanisms against adversarial attacks and discuss key challenges in securing machine learning systems.\n\nSpeaker Bio: Dr. Yong Wang is a Professor and Chair in the Department of Computer Science at the University of Idaho. He holds a Ph.D. in Computer Science from the University of Nebraska–Lincoln and has over a decade of experience in the telecommunications industry prior to transitioning to academia. His research focuses on security and privacy in IoT, cyber-physical systems, cyberinfrastructure, and adversarial ML. He has published over 100 peer-reviewed papers and secured more than $2.5 million in research funding. Dr. Wang also serves as a commissioner for the ABET Computing Accreditation Commission. He is dedicated to student success, fostering collaboration, and driving innovation in computer science education and research.
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