IEICE TRANSACTIONS on Information
Partition-then-Overlap Method for Labeling Cyber Threat Intelligence Reports by Topics over Time
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E104-D No.5 pp.556-561
- Publication Date
- 2021/05/01
- Publicized
- 2021/02/24
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020DAL0002
- Type of Manuscript
- Special Section LETTER (Special Section on Data Engineering and Information Management)
- Category
Authors
Ryusei NAGASAWA
Kobe University
Keisuke FURUMOTO
National Institute of Information and Communications Technology
Makoto TAKITA
University of Hyogo
Yoshiaki SHIRAISHI
Kobe University
Takeshi TAKAHASHI
National Institute of Information and Communications Technology
Masami MOHRI
Gifu University
Yasuhiro TAKANO
Kobe University
Masakatu MORII
Kobe University
Keyword
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Ryusei NAGASAWA, Keisuke FURUMOTO, Makoto TAKITA, Yoshiaki SHIRAISHI, Takeshi TAKAHASHI, Masami MOHRI, Yasuhiro TAKANO, Masakatu MORII, "Partition-then-Overlap Method for Labeling Cyber Threat Intelligence Reports by Topics over Time" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 5, pp. 556-561, May 2021, doi: 10.1587/transinf.2020DAL0002.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020DAL0002/_p
Copy
@ARTICLE{e104-d_5_556,
author={Ryusei NAGASAWA, Keisuke FURUMOTO, Makoto TAKITA, Yoshiaki SHIRAISHI, Takeshi TAKAHASHI, Masami MOHRI, Yasuhiro TAKANO, Masakatu MORII, },
journal={IEICE TRANSACTIONS on Information},
title={Partition-then-Overlap Method for Labeling Cyber Threat Intelligence Reports by Topics over Time},
year={2021},
volume={E104-D},
number={5},
pages={556-561},
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.},
keywords={},
doi={10.1587/transinf.2020DAL0002},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Partition-then-Overlap Method for Labeling Cyber Threat Intelligence Reports by Topics over Time
T2 - IEICE TRANSACTIONS on Information
SP - 556
EP - 561
AU - Ryusei NAGASAWA
AU - Keisuke FURUMOTO
AU - Makoto TAKITA
AU - Yoshiaki SHIRAISHI
AU - Takeshi TAKAHASHI
AU - Masami MOHRI
AU - Yasuhiro TAKANO
AU - Masakatu MORII
PY - 2021
DO - 10.1587/transinf.2020DAL0002
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2021
AB - 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.
ER -
English
