Vulnerability of Machine Learning Algorithms to Adversarial Attacks for Cyber-Physical Power Systems

Explore the critical topic of adversarial attacks on machine learning algorithms in cyber-physical power systems through this informative 32-minute talk by Tapadhir Das, a PhD Candidate from the Department of Computer Science and Engineering at the University of Nevada, Reno. Gain insights into the vulnerabilities of AI-driven systems in the power sector and understand the potential risks posed by malicious actors exploiting these weaknesses. Learn about cutting-edge research in this field and discover strategies to enhance the resilience of machine learning models against adversarial threats in critical infrastructure.