Using convolutional neural networks for classification of malware represented as images

Abstract

Malware authors introduced obfuscation techniques to existing malware in order to evade detection and hide its purposes. As a result, the number of malicious programs has grown in both volume and sophistication. Thus, effective categorization of malware based on its characteristics and behavior is required. In this paper, malicious software is visualized as gray scale images since its ability to capture minor changes while retaining the global structure helps to detect variations. Motivated by the visual similarity between malware samples of the same family, we propose a file agnostic deep learning approach for malware categorization to efficiently group malicious software into families based on a set of discriminant patterns extracted from their visualization as images. The suitability of our approach is evaluated against two benchmarks: the MalImg dataset and the BigData Innovators Gathering. Experimental comparison demonstrates its superior performance with respect to state-of-the-art techniques.

Using Transfer Learning for Malware Classification

“…(Xiao et al 2020) After they displayed the binary malware as entropy graphs they used deep learning to do feature extraction automatically and then used SVM to classify the malware based on the extracted features. (Gibert et al 2019) Based on the presentation of malware as an image, the following work presents a convolutional neural network (CNN) composed of three convolution layers followed by a fully-connected layer used for the classification of malware. They made a comparative study to prove that CNN has better results than KNN.…”

Parallel‐CNN network for malware detection

“…Gibert et al [24] developed a file agnostic deep learning system that learns visual features from executable files for classifying malware into different families. Their idea was motivated by the visual similarity between malware samples of the same family.…”

Mal2d: 2d Based Deep Learning Model for Malware Detection Using Black and White Binary Image

“…You may confuse with malware classification problems, to classify what type of malware is for a given malware. Some deep learning approaches for malware classification have been researched seperately [11], [12].…”