This webinar provides an overview of Adversarial Machine Learning (AML), its relationship to Generative (Deep) Learning, and ways to view AML as a potential enabler for deploying more comprehensive system-level Machine Learning capabilities. The basic ideas driving AML and the system-level architecture needs of an effective integrated ML capability are compared to find areas of commonality and future utility beyond single-shot, algorithm-by-algorithm approaches to AML and remediation techniques.
Presenter:
Michael Weir is currently working with Quanterion Solutions, Inc as a Senior Technical Advisor/Subject Matter Expert for the Cybersecurity and Information Systems Information Analysis Center (CSIAC), and with the Griffiss Institute as the developer/facilitator for the AFRL-sponsored Machine Learning Bootcamp, a multi-month immersion program for AFRL engineers. Mr. Weir was previously the Director of the CSIAC, and before that the Chief of Communications and Information Systems at the Eastern Air Defense Sector (EADS), Rome, New York. EADS is one of two NORAD/NORTHCOM air defense sectors in the Continental United States. He was responsible for setting up and maintaining the Sector's cyber posture during and after 9/11 and evolving the data/communication/sensor integration through the following decade. Mr. Weir has Bachelor's degrees in Music Performance and in Electrical and Computer Engineering, and a Master's Degree in Information Systems, along with certifications in the cybersecurity domain.