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Published on Dec 10, 2015
How Machine Learning Finds Malware Needles in an AppStore Haystack
Theodora Titonis, Vice President of Mobile Security at Veracode
Machine learning techniques are becoming more sophisticated. Can these techniques be more affective at assessing mobile apps for malicious or risky behaviors than traditional means? This session will include a live demo showing data analysis techniques and the results machine learning delivers in terms of classifying mobile applications with malicious or risky behavior. The presentation will also explain the difference between supervised and unsupervised algorithms used for machine learning as well as explain how you can use unsupervised machine learning to detect malicious or risky apps.
What you will learn:
Understand the difference between advanced machine learning techniques vs. traditional means. Recognize different types of algorithms used to improve mobile security. Understand how you can use unsupervised machine learning to detect malicious or risky apps. Theodora Titonis Theodora is an innovative entrepreneur whose passion for technology began when she started programming computers at the age of seven. While pursuing computer science at The Ohio State University she focused her efforts on the challenging field of security. During the dotcom-era, Theodora architected systems and provided security expertise to federal government intelligence and defense agencies, leading financial institutions and Fortune 500 Companies.
Theodora served as the Founder, CEO, sole investor, and a patent assignee of Marvin Mobile. Veracode, Inc., the leader in cloud-based application security testing, acquired Marvin in September 2012. Ms. Titonis now serves as Veracode's Vice President of Mobile Security.