Malware Analysis and Classification Using Machine Learning

Speaker:  David Mohaisen – Orlando, FL, United States
Topic(s):  Security and Privacy


Malicious software (or malware) is a vehicle for adversaries to launch various types of attacks, and there has been a constant steam of malware samples in the wild over the past few years. Per one study, the number of malware samples have grown to almost 1.1 billion samples in 2020, compared to 100 million samples only 8 years earlier, and attacks launched by malware has significant costs to the world economy, in order of hundreds of billions of dollars. This rise in this attack vector, coupled with the deployed of new systems of unprecedented scale, e.g., Internet of Things, call for techniques to identify malware samples for detection and classification. In this regard, machine learning has shown some promise, including significant accuracy results for filtering unwanted families, as well as operational systems for tracking families of interest overtime, or just making use of threat intelligence to reduce the manual analysis efforts. In this talk, we will review some of the recent results on the applications of machine learning for a broad class of malware analysis, detection, and classification using various program analysis modalities, such as strings, graphs, and functions. We will then explore the robustness of such defenses to a new class of attacks on machine learning and broad directions to defenses.

About this Lecture

Number of Slides:  45
Duration:  60 minutes
Languages Available:  English
Last Updated: 

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