Understanding Attack Trends from Security Blog Posts Using Guided-topic Model
この論文をさがす
抄録
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 ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.27(2019) (online)DOI http://dx.doi.org/10.2197/ipsjjip.27.802------------------------------
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 ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.27(2019) (online)DOI http://dx.doi.org/10.2197/ipsjjip.27.802------------------------------
収録刊行物
-
- 情報処理学会論文誌
-
情報処理学会論文誌 60 (12), 2019-12-15
- Tweet
詳細情報 詳細情報について
-
- CRID
- 1050845763997107072
-
- NII論文ID
- 170000181209
-
- NII書誌ID
- AN00116647
-
- ISSN
- 18827764
-
- Web Site
- http://id.nii.ac.jp/1001/00201438/
-
- 本文言語コード
- en
-
- 資料種別
- journal article
-
- データソース種別
-
- IRDB
- CiNii Articles