Understanding Attack Trends from Security Blog Posts Using Guided-topic Model

• Item type: Journal(1)
• 公開日: 2019-12-15
• タイトル: Understanding Attack Trends from Security Blog Posts Using Guided-topic Model
• タイトル / 言語: en
• タイトル: Understanding Attack Trends from Security Blog Posts Using Guided-topic Model
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集：ユーザブルセキュリティ] security blog post, topic model, threat analysis, incident handling
• 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: journal article
• 著者所属: Kobe University
• 著者所属: Kobe University／Presently with University of Hyogo
• 著者所属: National Institute of Information and Communications Technology
• 著者所属: Kobe University
• 著者所属: Nanyang Technological University
• 著者所属: Kobe University
• 著者所属: Gifu University
• 著者所属: Kobe University
• 著者所属(英): en
• 著者所属(英): Kobe University
• 著者所属(英): en
• 著者所属(英): Kobe University / Presently with University of Hyogo
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology
• 著者所属(英): en
• 著者所属(英): Kobe University
• 著者所属(英): en
• 著者所属(英): Nanyang Technological University
• 著者所属(英): en
• 著者所属(英): Kobe University
• 著者所属(英): en
• 著者所属(英): Gifu University
• 著者所属(英): en
• 著者所属(英): Kobe University
• 著者名: Tatsuya, Nagai; Makoto, Takita; Keisuke, Furumoto; Yoshiaki, Shiraishi; Kelin, Xia; Yasuhiro, Takano; Masami, Mohri; Masakatu, Morii
• 著者名(英): Tatsuya, Nagai; Makoto, Takita; Keisuke, Furumoto; Yoshiaki, Shiraishi; Kelin, Xia; Yasuhiro, Takano; Masami, Mohri; Masakatu, Morii
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Organizations are plagued by sophisticated and diversified cyber attacks. In order to prevent such attacks, it is necessary to understand threat trends and to take measures to protect their assets. Security vendors publish reports which contain threat trends or analysis of malware. These reports are useful for help in responding to a cyber security incident. However, it is difficult to collect threat information from multiple sources such as security blog posts. In this paper, we propose a method to efficiently collect information from the relationship between words using SeededLDA. In our case studies, we visualize the relationship between the words from security blog posts which were published in 2017 by eight security vendors, and demonstrate how our method helps to understand threat trends in the IoT industry and financial institutions.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.27(2019) (online); DOI http://dx.doi.org/10.2197/ipsjjip.27.802; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Organizations are plagued by sophisticated and diversified cyber attacks. In order to prevent such attacks, it is necessary to understand threat trends and to take measures to protect their assets. Security vendors publish reports which contain threat trends or analysis of malware. These reports are useful for help in responding to a cyber security incident. However, it is difficult to collect threat information from multiple sources such as security blog posts. In this paper, we propose a method to efficiently collect information from the relationship between words using SeededLDA. In our case studies, we visualize the relationship between the words from security blog posts which were published in 2017 by eight security vendors, and demonstrate how our method helps to understand threat trends in the IoT industry and financial institutions.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.27(2019) (online); DOI http://dx.doi.org/10.2197/ipsjjip.27.802; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 60, 号 12, 発行日 2019-12-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764