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IoTマルウェア検知精度向上のためのGridSearchCVによるOCSVMのパラメータ最適化
http://hdl.handle.net/10458/00010280
http://hdl.handle.net/10458/00010280d6e1adbd-fcb2-41c9-9bbc-eb03a24faf2c
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
本文 (1.7 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 | IoTマルウェア検知精度向上のためのGridSearchCVによるOCSVMのパラメータ最適化 | |||||
| 言語 | ja | |||||
| タイトル | ||||||
| タイトル | Parameter Optimization of OCSVM by GridSearchCV for Improving IoT Malware Detection Accuracy | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | jpn | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | IoT | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Malware detection | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | OCSVM | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | GridSearchCV | |||||
| 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 |
村中, 弘樹
× 村中, 弘樹× 後藤, 修斗× 油田, 健太郎× 山場, 久昭× 岡崎, 直宣× Muranaka, Hiroki× Goto, Shuto |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Abstract is written in 200-300 words. In recent years, the Internet of Things (IoT) has been playing an increasingly important role in our lives. It has enabled us to form new services and business models, and to automate and improve the efficiency of our work. However, at the same time, security vulnerabilities have become an issue: according to NICT, more than half of the observed cyber attack-related communications targeted the IoT [3].In this study, we propose a system that combines n-gram analysis and OCSVM to discriminate between normal communication of IoT devices and communication after malware infection, and a system that uses GridSearchCV to calculate the best parameters for the parameters arbitrarily set by humans when applying OCSVM. The system is proposed to calculate the best parameters using GridSearchCV. For the evaluation, we conducted an experiment to compare the detection accuracy of 25 sets of parameters conventionally used and the detection accuracy using GridSearchCV, and showed good detection accuracy. However, in some cases, the detection accuracy decreased in the process of increasing the total number of data given to GridSearchCV. As a future subject, it is necessary to change the ratio and the total number of normal data and more than normal data, to observe the change of detection accuracy, and to study the cause. | |||||
| 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. 131-135, 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 | |||||
