@article{oai:miyazaki-u.repo.nii.ac.jp:00006425,
 author = {川畑, 魁星 and Aburada, Kentaro and 油田, 健太郎 and Yamaba, Hisaaki and 山場, 久昭 and Okazaki, Naonobu and 岡崎, 直宣 and Kawabata, Kaisei},
 journal = {宮崎大学工学部紀要, Memoirs of Faculty of Engineering, University of Miyazaki},
 month = {Sep},
 note = {In recent years, the spread of malware has become a threat to computer security. The existence of malware variants is a factor that has a significant impact on the increase in the number of malware discoveries. Research has been conducted to automatically and efficiently classify these variants of malware. With the development of deep learning, it is now used to classify subspecies of malware. A typical research is to convert malware into grayscale images and classify them using CNN (Convolutional neural network). In deep learning, a large amount of training data is used. However, when a new type of malware appears, it is difficult to collect a large amount of samples. In this research, we investigated whether it is possible to solve the problem of insufficient samples by generating training data for deep learning using GAN (Generative Adversarial Network) and extending the data. We conducted an experiment to see if the classification accuracy could be improved by expanding the data for training using GAN. We used datasets that consisted of 25 different malware families. It was confirmed that the classification accuracy was improved compared to that before the data expansion. From the results, it was found that the data expansion for malware classification using GAN was effective.},
 pages = {155--159},
 title = {CNNによるマルウェア分類を改善するためのGANを用いたデータ拡張},
 volume = {50},
 year = {2021},
 yomi = {カワバタ, カイセイ and アブラダ, ケンタロウ and ヤマバ, ヒサアキ and オカザキ, ナオノブ}
}