Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
Arguably, the two domains closely related to information technology recently gaining the
most attention are ‘cybersecurity’ and ‘data science’. Yet, the intersection of both domains
often faces the conundrum of discussions intermingled with ill-understood concepts and
terminologies. A topic model is desired to illuminate significant concepts and terminologies,
straddling in cybersecurity and data science. Also, the hope exists to knowledge-discover
under-researched topics and concepts, yet deserving more attention for the intersection
crossing both domains. Motivated by these, then retaining most of the already accepted IMCIC
(the International Multi-Conference on Complexity, Informatics, and Cybernetics) 2019
conference paper’s content and supplementing it with implicit design activities while
conducting the research, this study attempts to take on a challenge to model cybersecurity and
data science topics clustered with significant concepts and terminologies, grounded on a textmining
approach based on the recent scholarly articles published between 2012 and 2018. As
the means to the end of modeling topic clusters, the research is approached with a text-mining
technique, comprised of key-phrases extraction, topic modeling, and visualization. The trained
LDA Model in the research analyzed and generated significant terms from the text-corpus from
48 articles and found that six latent topic clusters comprised the key terms. Afterwards, the
researchers labeled the six topic clusters for future cybersecurity and data science researchers
as follows: Advanced/Unseen Attack Detection, Contextual Cybersecurity, Cybersecurity
Applied Domain, Data-Driven Adversary, Power System in Cybersecurity, and Vulnerability
Management. The subsequent qualitative evaluation of the articles found the LDA Model
supplied the six topic clusters in unveiling latent concepts and terminologies in cybersecurity
and data science to enlighten both domains. The main contribution of this research is the
identification of key concepts in the topic clusters and text-mining key-phrases from the recent
scholarly articles focusing on cybersecurity and data science. By undertaking this research,
this study aims to advance the fields of cybersecurity and data science. Besides the main
contribution, the additional research contributions are as follows: First, the topic modeling
approached using text-mining makes the cybersecurity domain unearth the terminologies that
make IST (Information Systems and Technology) researchers investigate further. Secondly,
using the result of the study’s analysis, IST researchers can decide terms of interest and further
investigate the articles that supplied the terms.
