2022-05-19, 16:35–17:05, Ville-Marie
In this work we build a machine learning classifier that distinguishes between cleartext and obfuscated code.
This allows us to extract several families of features. Some of them require careful feature engineering, while others are more general and follow well-known NLP techniques.
Next, we survey prior art from the literature and discuss several natural approaches to this problem.
Finally, we suggest obfuscator-agnostic methods to build state-of-the-art machine learning classifier for this problem.
As a data scientist in Imperva, I develop machine learning solutions for various cyber security projects.
I'm fascinated by the wonders that data science and machine learning bring to the world. The wealth of open-source frameworks enable us to build systems today at scale and ease unthinkable just several years ago.
In the last 20+ years I've been working in the hi-tech industry in Israel. I am lucky to have worked for several great companies in engineering, management and research positions.
I hold an M.Sc. in Computer Science from the Weizmann Institute in Israel.