Efficient Malware Classification by Binary Sequences with One-Dimensional Convolutional Neural Networks

The rapid increase of malware attacks has become one of the main threats to computer security. Finding the best way to detect malware has become a critical task in cybersecurity. Previous work shows that machine learning approaches could be a solution to address this problem. Many proposed methods convert malware executables into grayscale images and apply convolutional neural networks (CNNs) for malware classification. However, converting malware executables into images could twist the one-dimensional structure of binary codes. To address this problem, we explore the bit and byte-level sequences from malware executables and propose efficient one-dimensional (1D) CNNs for the malware classification. Our experiments evaluate our proposed 1D CNN models with two benchmark datasets. Our proposed 1D CNN models achieve better performance from the experimental results than the existing 2D CNNs malware classification models by providing smaller resizing bit/byte-level sequences with less computational cost.

Automated summary

This study explores extracting bit and byte-level sequences from malware executables and proposes an efficient one-dimensional CNN model for malware classification. Experimental results show that our proposed 1D CNN models outperform existing 2D CNN models for malware classification by providing better performance with smaller resizing bit/byte-level sequences and less computational cost.