API based sequence and statistical features in a combined malware detection architecture

LU Xiaofeng; JIANG Fangshuo; ZHOU Xiao; CUI Baojiang; YI Shengwei; SHA Jing

doi:10.16511/j.cnki.qhdxxb.2018.25.020

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Journal of Tsinghua University(Science and Technology) ›› 2018, Vol. 58 ›› Issue (5) : 500-508. DOI: 10.16511/j.cnki.qhdxxb.2018.25.020

COMPUTER SCIENCE AND TECHNOLOGY

API based sequence and statistical features in a combined malware detection architecture

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Abstract

This paper presents a combined machine learning framework for malware behavior analyses. One part of the framework analyzes the dependency relation in the API call sequence at the functional level to extract features to train and classify a random forest. The other part uses a recurrent neural network (RNN) to study the API sequence to identify malware with redundant information preprocessing using the RNN time series forecasting ability. Tests on a malware dataset show that both methods can effectively detect malwares. However, the combined framework is better with an AUC of 99.3%.

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computer virus and prevention / malware classification / machine learning / deep learning / call sequence

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LU Xiaofeng, JIANG Fangshuo, ZHOU Xiao, CUI Baojiang, YI Shengwei, SHA Jing. API based sequence and statistical features in a combined malware detection architecture[J]. Journal of Tsinghua University(Science and Technology). 2018, 58(5): 500-508 https://doi.org/10.16511/j.cnki.qhdxxb.2018.25.020

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