@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00208510,
 author = {Yun, Gao and Hirokazu, Hasegawa and Yukiko, Yamaguchi and Hajime, Shimada and Yun, Gao and Hirokazu, Hasegawa and Yukiko, Yamaguchi and Hajime, Shimada},
 book = {コンピュータセキュリティシンポジウム2020論文集},
 month = {Oct},
 note = {The increasing number of malicious software spread through the Internet has become a serious threat. Malware authors use obfuscation and deformation techniques to generate new types of malware in order to evade the detection of traditional detection methods, so that it is widely expected for machine learning methods that classifies malware and cleanware based on the characteristics of the samples. The current research trend is to use machine learning technology, especially decision tree technology, to identify new malicious software quickly and accurately. The purpose of this paper is to investigate malware classification accuracy based on latest decision tree based algorithms including ensemble learning. Therefore, we use the FFRI Dataset 2019 to construct malware detection models from surface analysis logs and PE header dumps. We have successfully developed a malware detection model that is more accurate than previous studies. We have obtained good classification results using only 27 features.},
 pages = {589--595},
 publisher = {情報処理学会},
 title = {Gradient Boosting Decision Tree Ensemble Learning for Malware Binary Classification},
 year = {2020}
}