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.

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