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

Ryusei NAGASAWA; Keisuke FURUMOTO; Makoto TAKITA; Yoshiaki SHIRAISHI; Takeshi TAKAHASHI; Masami MOHRI; Yasuhiro TAKANO; Masakatu MORII

doi:10.1587/transinf.2020DAL0002

Special Section on Data Engineering and Information Management

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

Ryusei NAGASAWA, Keisuke FURUMOTO, Makoto TAKITA, Yoshiaki SHIRAISHI, Takeshi TAKAHASHI, Masami MOHRI, Yasuhiro TAKANO, Masakatu MORII

Author information

Ryusei NAGASAWA
Department of Electrical and Electronic Engineering, Kobe University
Keisuke FURUMOTO
National Institute of Information and Communications Technology
Makoto TAKITA
School of Social Information Science, University of Hyogo
Yoshiaki SHIRAISHI
Department of Electrical and Electronic Engineering, Kobe University
Center for Mathematical and Data Sciences, Kobe University
Takeshi TAKAHASHI
National Institute of Information and Communications Technology
Masami MOHRI
Department of Electrical, Electronic and Computer Engineering, Gifu University
Yasuhiro TAKANO
Department of Electrical and Electronic Engineering, Kobe University
Masakatu MORII
Department of Electrical and Electronic Engineering, Kobe University

Keywords: topic model, cyber threat intelligence, text mining, multi-labeling, security blog posts

JOURNAL FREE ACCESS

2021 Volume E104.D Issue 5 Pages 556-561

DOI https://doi.org/10.1587/transinf.2020DAL0002

Details

Abstract

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.

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