| Item type |
Symposium(1) |
| 公開日 |
2019-10-14 |
| タイトル |
|
|
タイトル |
Quantifying the Significance of Cybersecurity Related Text Documents by Analyzing IoC and Named Entities |
| タイトル |
|
|
言語 |
en |
|
タイトル |
Quantifying the Significance of Cybersecurity Related Text Documents by Analyzing IOC and Named Entities |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
cyber threat，text analytic，IOC，NER |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_5794 |
|
資源タイプ |
conference paper |
| 著者所属 |
|
|
|
Nagoya University |
| 著者所属 |
|
|
|
Nagoya University |
| 著者所属 |
|
|
|
Nagoya University |
| 著者所属 |
|
|
|
Nagoya University |
| 著者所属(英) |
|
|
|
en |
|
|
Nagoya University |
| 著者所属(英) |
|
|
|
en |
|
|
Nagoya University |
| 著者所属(英) |
|
|
|
en |
|
|
Nagoya University |
| 著者所属(英) |
|
|
|
en |
|
|
Nagoya University |
| 著者名 |
Otgonpurev, Mendsaikhan
Hirokazu, Hasegawa
Yukiko, Yamaguchi
Hajime, Shimada
|
| 著者名(英) |
Otgonpurev, Mendsaikhan
Hirokazu, Hasegawa
Yukiko, Yamaguchi
Hajime, Shimada
|
| 論文抄録 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
In order to proactively mitigate the cybersecurity risks, the security analysts have to continuously monitor the threat information sources. However the sheer amount of textual information that needs to be processed is overwhelming and requires a mundane labor. We propose a novel approach to automate this process by analyzing and enriching textual cyber threat information using the number of Indicator of Compromise (IOC) and number of Common Vulnerabilities and Exposure (CVE) associated with the content of the information. By fine-tuning the pre-trained Named Entity Recognition model in cybersecurity domain and utilizing various Open Source Intelligence sources to validate the IOCs found in the text we were able to perform experiments and obtain preliminary results. |
| 論文抄録(英) |
|
|
内容記述タイプ |
Other |
|
内容記述 |
In order to proactively mitigate the cybersecurity risks, the security analysts have to continuously monitor the threat information sources. However the sheer amount of textual information that needs to be processed is overwhelming and requires a mundane labor. We propose a novel approach to automate this process by analyzing and enriching textual cyber threat information using the number of Indicator of Compromise (IOC) and number of Common Vulnerabilities and Exposure (CVE) associated with the content of the information. By fine-tuning the pre-trained Named Entity Recognition model in cybersecurity domain and utilizing various Open Source Intelligence sources to validate the IOCs found in the text we were able to perform experiments and obtain preliminary results. |
| 書誌レコードID |
|
|
|
識別子タイプ |
NCID |
|
|
関連識別子 |
ISSN 1882-0840 |
| 書誌情報 |
コンピュータセキュリティシンポジウム2019論文集
巻 2019,
p. 1378-1383,
発行日 2019-10-14
|
| 出版者 |
|
|
言語 |
ja |
|
出版者 |
情報処理学会 |
IPSJCSS2019194.pdf (485.6 kB)
