A Malware Detection Approach Using Autoencoder in Deep Learning
Xiaofei Xing, Xiang Jin, Haroon Elahi, Hai Jiang, Guojun Wang
Abstract
Today, in the field of malware detection, the expanding limitations of traditional detection methods and the increasing accuracy of detection methods designed on the basis of artificial intelligence algorithms are driving research findings in this area in favour of the latter. Therefore, we propose a novel malware detection model in this paper. This model combines a grey-scale image representation of malware with an autoencoder network in a deep learning model, analyses the feasibility of the grey-scale image approach of malware based on the reconstruction error of the autoencoder, and uses the dimensionality reduction features of the autoencoder to achieve the classification of malware from benign software. The proposed detection model achieved an accuracy of 96% and a stable F-score of about 96% by using the Android-side dataset we collected, which outperformed some traditional machine learning detection algorithms.