A Convolutional Transformation Network for Malware Classification

TL;DR

This paper introduces a novel hybrid image transformation and deep convolutional neural network approach for malware classification, achieving high accuracy by effectively conveying binary semantics through transformed images.

Contribution

The paper presents a new hybrid image transformation method combined with a deep CNN for malware detection, improving accuracy over existing simple transformation techniques.

Findings

Achieves 99.14% accuracy on malware classification

Outperforms all baseline methods in experiments

Introduces a novel binary-to-image transformation conveying semantics

Abstract

Modern malware evolves various detection avoidance techniques to bypass the state-of-the-art detection methods. An emerging trend to deal with this issue is the combination of image transformation and machine learning techniques to classify and detect malware. However, existing works in this field only perform simple image transformation methods that limit the accuracy of the detection. In this paper, we introduce a novel approach to classify malware by using a deep network on images transformed from binary samples. In particular, we first develop a novel hybrid image transformation method to convert binaries into color images that convey the binary semantics. The images are trained by a deep convolutional neural network that later classifies the test inputs into benign or malicious categories. Through the extensive experiments, our proposed method surpasses all baselines and achieves 99.14% in terms of accuracy on the testing set.