Partition-then-Overlap Method for Labeling Cyber Threat Intelligence Reports by Topics over Time

被引：2

作者：

Nagasawa, Ryusei ^{[1
]}

Furumoto, Keisuke ^{[2
]}

Takita, Makoto ^{[3
]}

Shiraishi, Yoshiaki ^{[1
,4
]}

Takahashi, Takeshi ^{[2
]}

Mohri, Masami ^{[5
]}

Takano, Yasuhiro ^{[1
]}

Morii, Masakatu ^{[1
]}

机构：

[1] Kobe Univ, Dept Elect & Elect Engn, Kobe, Hyogo 6578501, Japan

[2] Natl Inst Informat & Commun Technol, Koganei, Tokyo 1848795, Japan

[3] Univ Hyogo, Sch Social Informat Sci, Kobe, Hyogo 6512197, Japan

[4] Kobe Univ, Ctr Math & Data Sci, Kobe, Hyogo 6578501, Japan

[5] Gifu Univ, Dept Elect Elect & Comp Engn, Gifu 5011193, Japan

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2021年 / E104D卷 / 05期

关键词：

topic model; cyber threat intelligence; text mining; multi-labeling; security blog posts; EXTRACTION;

D O I：

10.1587/transinf.2020DAL0002

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The Topics over Time (TOT) model allows users to be aware of changes in certain topics over time. The proposed method inputs the divided dataset of security blog posts based on a fixed period using an overlap period to the TOT. The results suggest the extraction of topics that include malware and attack campaign names that are appropriate for the multi-labeling of cyber threat intelligence reports.

引用

页码：556 / 561

页数：6

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