A Multi-Attribute Decision Making Model for Selection of Cybersecurity Candidates in an R&D Organization Open Access
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The cybersecurity profession means many things to many people. This lack of consensus has led to hiring decision based on intuition and personal preference for cybersecurity research and development (R&D;) positions. To improve communication with potential hires, focus hiring efforts, and reduce hiring based on intuition in this field, hiring managers must identify, prioritize, and weight the cybersecurity Tasks, Knowledge, Skills, and Abilities (tKSA) most important to their organization. This praxis uses the tKSAs defined by the National Initiative for Cybersecurity Education (NICE) Cybersecurity Workforce Development Framework for the role of cybersecurity R&D; specialist. It then determined how important a leading R&D; institute considers each of these tKSAs to be. The praxis developed a Likert-scale survey to gather the opinions of subject matter experts (SME) on the tKSAs and have them weight the importance of each. The results show no significant difference in the attribute preferences of the institute’s organizational components. To support hiring decisions and evaluation of candidates, this praxis developed a decision support tool that utilizes the multi-attribute decision making (MADM) method TOPSIS.