WEKO3
アイテム
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
http://hdl.handle.net/10458/00010284
http://hdl.handle.net/10458/000102848c83db38-b12a-4e88-acd5-2d1bc3710bca
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
本文 (1.9 MB)
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| Item type | 紀要論文 / Departmental Bulletin Paper(1) | |||||
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| 公開日 | 2021-11-02 | |||||
| タイトル | ||||||
| タイトル | 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 | |||||
| 言語 | ja | |||||
| タイトル | ||||||
| タイトル | Data Expansion Using GAN to Improve Malware Classification by CNN | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | jpn | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Deep learning | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Malware | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Generative adversarial network | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Data expansion | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | departmental bulletin paper | |||||
| 著者 |
川畑, 魁星
× 川畑, 魁星× 油田, 健太郎× 山場, 久昭× 岡崎, 直宣× Kawabata, Kaisei |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | 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. | |||||
| 言語 | en | |||||
| 書誌情報 |
ja : 宮崎大学工学部紀要 en : Memoirs of Faculty of Engineering, University of Miyazaki 巻 50, p. 155-159, 発行日 2021-09-28 |
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| 出版者 | ||||||
| 出版者 | 宮崎大学工学部 | |||||
| 言語 | ja | |||||
| 出版者 | ||||||
| 出版者 | Faculty of Engineering, University of Miyazaki | |||||
| 言語 | en | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 05404924 | |||||
| 書誌レコードID | ||||||
| 収録物識別子タイプ | NCID | |||||
| 収録物識別子 | AA00732558 | |||||
| 著者版フラグ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
