Speaker: Dr. Edward Raff, Booz Allen Hamilton. Visiting Professor, UMBC Dept. of Computer Science

Abstract: Malware is an ever growing problem, single malware families have caused billions in damages and the first direct death attributed to malware taking down a hospital has occurred. To detect new malware, machine learning is a naturally attractive approach. However, malware poses a number of unique challenges that have slowed the progress of ML based solutions. In this talk we will look at the task of malware detection from byte based analysis, why is poses many challenging ML research problems, and progress we have made on these tasks by taking some non-standard approaches to machine learning: building shallow and wide networks instead of deep, handicapping the features of our model to make it robust, and using literal compression algorithms (LZMA) to find similar content.

Bio: Edward Raff leads Booz Allen's machine learning research group and supports clients developing new ML solutions. His research includes cyber security, adversarial machine learning, fairness and ethics, fingerprint biometrics, and high-performance computing. In his spare time, he is the author of the JSAT machine learning library. He received his BS and MS in Computer Science from Purdue University, and his PhD in CS from UMBC. Dr. Raff is a Nvidia Deep Learning certified instructor, and Visiting Professor at UMBC.

Time: Wednesday, February 17, 12-1pm ET.

Location: WebEx, meeting link: https://umbc.webex.com/umbc/j.php?MTID=m87807fcfe1c446dd14e68ef9371aa494  

Meeting number: 120 701 4260

Password: ugUaz6rH