Investigating Cybersecurity News Articles by Applying Topic Modeling Method

Piyush Ghasiya, Koji Okamura

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Machine Learning (ML) and specifically Natural Language Processing (NLP) are increasingly used as tools in the cybersecurity world. These NLP tools bring new capabilities that support both defenders and attackers in their activities, whether it is risk scenarios such as events and threats or security operations. Ours is a unique case study as we are investigating cybersecurity news on a national and global level. This large study covered six countries and 18 major newspapers and analyzed thousands of cybersecurity articles using the Nonnegative Matrix Factorization (NMF) topic modeling method. News making and policymaking complement each other in forming national identities. This research aims to provide the foundation for the field of Cybersecurity in this direction. Our results showed the US dominance and its significance for other countries. This research also highlighted that much of the US media's cybersecurity reporting focuses on domestic issues, unlike other nations.

本文言語英語
ホスト出版物のタイトル35th International Conference on Information Networking, ICOIN 2021
出版社IEEE Computer Society
ページ432-438
ページ数7
ISBN(電子版)9781728191003
DOI
出版ステータス出版済み - 1 13 2021
イベント35th International Conference on Information Networking, ICOIN 2021 - Jeju Island, 大韓民国
継続期間: 1 13 20211 16 2021

出版物シリーズ

名前International Conference on Information Networking
2021-January
ISSN(印刷版)1976-7684

会議

会議35th International Conference on Information Networking, ICOIN 2021
Country大韓民国
CityJeju Island
Period1/13/211/16/21

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems

フィンガープリント 「Investigating Cybersecurity News Articles by Applying Topic Modeling Method」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル