Machine Learning Security in Cyber-Physical Systems
Machine learning algorithms are susceptible to adversarial attacks that cause the model to output wrong results by polluting the training data. The problem is exacerbated when the machine learning algorithms are used by critical infrastructures such as the power grid and transportation systems. We demonstrate the insecurity of machine learning applied to these systems by developing realistic adversarial attacks.
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Registration
- Date: 20 Sep 2024
- Time: 11:00 AM to 12:00 PM
- All times are (UTC-04:00) Eastern Time (US & Canada)
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- Toronto Metropolitan University
- Toronot, Ontario
- Canada M5B 2K3
- Building: VIC
- Room Number: 202
- Starts 17 September 2024 12:00 AM
- Ends 20 September 2024 12:00 AM
- All times are (UTC-04:00) Eastern Time (US & Canada)
- No Admission Charge
Speakers
Machine Learning Security in Cyber-Physical Systems
Biography:
Jinyuan "Stella" Sun is a Professor of Computer Science at the University of Tennessee. She obtained her PhD degree from University of Florida and M.A.Sci. from Toronto Metropolitan University. Her research lies in the general areas of cybersecurity and privacy and her current research interests include machine learning security and privacy, cyber-physical security, and mobile system security.