Deciphering published articles on cyberterrorism: a latent Dirichlet allocation algorithm application

Las Johansen Balios Caluza

International Journal of Data Mining, Modelling and Management, 2019, vol. 11, issue 1, 87-101

Abstract: An emerging issue called cyberterrorism is a fatal problem causing a disturbance in the cyberspace. To unravel underlying issues about cyberterrorism, it is imperative to look into available documents found in the NATO's repository. Extraction of articles using web-mining technique and performed topic modelling on NLP. Moreover, this study employed latent Dirichlet allocation algorithm, an unsupervised machine learning to generate latent themes from the text corpus. An identified five underlying themes revealed based on the result. Finally, a profound understanding of cyberterrorism as a pragmatic menace of the cyberspace through a worldwide spread of black propaganda, recruitment, computer and network hacking, economic sabotage and others revealed. As a result, countries around the world, including NATO and its allies, had continuously improved its capabilities against cyberterrorism.

Keywords: topic modelling; latent Dirichlet allocation; LDA; cyberterrorism; unsupervised machine learning; natural language processing; NLP; sequential exploratory design; Gibbs sampling; cyberspace; web mining. (search for similar items in EconPapers)
Date: 2019
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