A Comparative Study on the Application of Text Mining in Cybersecurity

Abstract

Aims: This paper aims to conduct a Systematic Literature Review (SLR) of the relative applications of text mining in cybersecurity. Objectives: worldwide has been attributed to a change in the different activities associated with cyber security and demands a high automation level. Methods: In the cyber security domain, text mining is an alternative for improving the usefulness of various activities that entail unstructured data. This study searched databases of 516 papers from 2015 to 21. Out of which 75 papers are selected for analysis. A detailed evaluation of the selected studies employs source, techniques, and information extraction on cyber security applications. Results: This study extends gaps for future study such as text processing, availability of datasets, innovative methods, intelligent text mining. Conclusion: This study concludes with interesting findings of employing text mining in cybersecurity applications; the researchers need to exploit all related techniques and algorithms in text mining to detect and protect the organization from Cybersecurity applications.