Malware Detection by Eating a Whole EXE

Code

• github.com/jaketae/pytorch-malware-detection
  pytorch★ 81
• github.com/dtrizna/quo.vadis
  pytorch★ 41
• github.com/Abdullah-B/Best-Effort-Adversarial-Approximation-of-Black-Box-Malware-Classifiers
  tf★ 0
• github.com/jaketae/deep-malware-detection
  pytorch★ 0
• github.com/iBibek/MalConv-Deep-learning-for-PE-malware-classification
  none★ 0
• github.com/pralab/toucanstrike
  none★ 0
• github.com/endgameinc/malware_evasion_competition
  pytorch★ 0

Abstract

In this work we introduce malware detection from raw byte sequences as a fruitful research area to the larger machine learning community. Building a neural network for such a problem presents a number of interesting challenges that have not occurred in tasks such as image processing or NLP. In particular, we note that detection from raw bytes presents a sequence problem with over two million time steps and a problem where batch normalization appear to hinder the learning process. We present our initial work in building a solution to tackle this problem, which has linear complexity dependence on the sequence length, and allows for interpretable sub-regions of the binary to be identified. In doing so we will discuss the many challenges in building a neural network to process data at this scale, and the methods we used to work around them.

Tasks

Reproductions