Cybersecurity Training Curriculum Analysis through the application of Machine Learning Text Classification and Natural Language Processing

Abstract

In the domain of cybersecurity, there are a wide variety of education and training courses offered by a variety of training providers (commercial vendors, governmental, and academic institutions). Unfortunately, a comprehensive cross-provider mapping of the body of course offerings does not currently exist. Furthermore, although individual providers have made efforts, an accepted cross-provider mapping of education and training courses (or categories) to an accepted framework of work-roles by cybersecurity work-role and competency level has not yet been developed. The inability to compare training courses by topic or cybersecurity work-role and the level of difficulty (competency level) affects all organizations in identifying and selecting potential training opportunities for their personnel performing cybersecurity duties. In this work, we report on our application of Machine Learning (ML) Text Classification and Natural Language Processing (NLP) methodologies to begin to create a process to organize an accurate and thorough catalog of course offerings by work-role and competency level. Through the analysis of text-based course attributes (description, learning objectives, prerequisites, etc.) along with the text-based attributes for identified cybersecurity work-roles, our ML efforts examined methods to determine the work-role "best-fit" for each course as well as the competency level through action-verb text comparison.