Deep Learning on Disassembly

by Matt Wolff, Andrew Davis Sept. 18, 2017 via Black Hat submitted by belen_caty

In this talk, we show the effectiveness of applying deep learning techniques to disassembly in an effort to generate models designed to identify malware. Starting with a brief explanation of deep learning, we then work through the different pieces of the pipeline to go from a collection of raw binaries, to extraction and transformation of disassembly data, and training of a deep learning model. We then conclude by providing data on the efficacy of these models, and follow up with a live demo where we will evaluate the models against active malware feeds.

Steven Ulm 1 month ago

Extremely interesting Disassembly related article! If the learning model is properly optimized , then the model will be efficient!