%0 Journal Article
%A HE Chao
%A LUO Yi
%A XU Xiao-bo
%A ZHANG Wen-bo
%T A Malicious Code Detection Method Based on Ensemble Learning of Behavior
%D 2019
%R 10.13190/j.jbupt.2018-318
%J Journal of Beijing University of Posts and Telecommunications
%P 89-95
%V 42
%N 4
%X In order to solve the problem of variant malicious code and behavior analysis of unknown threat, a method for malware classification based on gradient boosting decision tree (GBDT) algorithm is researched, which learns the characteristics of code behavior and instruction sequence from a large number of samples, and realizes the intelligent malicious code classification function. GBDT algorithm is introduced into the field of malicious code detection, so that the behavior sequence of the model is interpretable, and improves its ability to detect malicious code significantly. GBDT algorithm can reflect the nature of the behavior and intention of malicious code objectively, and identify malicious code accurately.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2018-318