Machine learning is being used to analyze large amounts of data in a wide variety of industries. While machine learning has many benefits, machine learning is also prone to being attacked. In this session, attendees will be introduced to the idea of adversarial machine learning and attacks to machine learning models. Attendees will learn about some real-world case studies regarding attacks that have impacted top companies in the industry as well as some current open-source industry solutions that aim to increase the security of machine learning algorithms. After the session, attendees will learn about the difference between AI and machine learning and open-source solutions that exist that aim to mitigate adversarial machine learning attacks.


July 12, 2024

3:40 pm


4:15 pm

Union AB

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Threat Analysis
Threat Analysis


Anmol Agarwal
Security Researcher


Dr. Anmol Agarwal is a security researcher at Nokia and is focused on securing AI and Machine Learning in 5G and 6G. She holds a doctoral degree in cybersecurity analytics from George Washington University and a master’s degree in computer science from the University of Texas at Dallas. She previously worked at the U.S. Cybersecurity and Infrastructure Security Agency (CISA). In her free time, she enjoys giving back to the community and is an active industry mentor. She will be representing herself at this event as an individual security researcher; she is not representing her employer.