@article{oai:ipsj.ixsq.nii.ac.jp:00201531,
 author = {Tatsuya, Nagai and Makoto, Takita and Keisuke, Furumoto and Yoshiaki, Shiraishi and Kelin, Xia and Yasuhiro, Takano and Masami, Mohri and Masakatu, Morii and Tatsuya, Nagai and Makoto, Takita and Keisuke, Furumoto and Yoshiaki, Shiraishi and Kelin, Xia and Yasuhiro, Takano and Masami, Mohri and Masakatu, Morii},
 issue = {12},
 journal = {情報処理学会論文誌},
 month = {Dec},
 note = {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
------------------------------, 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
------------------------------},
 title = {Understanding Attack Trends from Security Blog Posts Using Guided-topic Model},
 volume = {60},
 year = {2019}
}