基于合成图像和Xception改进模型的安卓恶意家族分类方法

doi:10.11896/jsjkx.220300200

Abstract

Abstract: Aiming at the problems in the field of Android malicious family detection,such as insufficient code visualization method construction information,large classification effect affected by the number of data sets and low classification accuracy,an Android malicious family classification method based on multi feature file synthetic image and Xception improved model is proposed.Fir-stly,three feature files corresponding to RGB multi-channel are selected to synthesize color images.Then,the improved Xception model introduces the focal loss function to alleviate the negative impact caused by the uneven distribution of samples.Finally,the attention mechanism is integrated into the improved model to extract the image features of malicious code from different dimensions,which improves the classification effect of the model.Experimental results show that the malicious code images synthesized by the proposed method contain richer features,have higher accuracy than the mainstream malicious family classification methods,and have better classification effect for unbalanced data sets.

Key words: Malware visualization, Android malware family classification, Attention mechanism, focal loss, Xception

CLC Number:

TP309

YU Xingzhan, LU Tianliang, DU Yanhui, WANG Xirui, YANG Cheng. Android Malware Family Classification Method Based on Synthetic Image and Xception Improved Model[J].Computer Science, 2023, 50(4): 351-358.

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Android Malware Family Classification Method Based on Synthetic Image and Xception Improved Model

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