Utilizing Association Rule Mining to Understand Phishing Risk Awareness Levels of Thai University Academic Staff - Jarupunphol, Pita; Buathong, Wipawan (Journal of Applied Informatics and Technology, 2025)
This study explores the phishing risk awareness levels among academic staff at Thai universities, employing association rule mining (ARM) to identify critical factors influencing high and low levels of awareness. Targeting a diverse group of 400 academic staff members, the research utilized a structured questionnaire comprising demographic information, direct and indirect experiences with phishing, and perceptions of phishing. In association rules ( ), a lift value of 1 indicates independence between X and Y, while values greater than 1 or less than 1 indicate positive or negative correlation, respectively. The findings revealed several critical findings: despite being able to define phishing, many individuals do not perceive it as a significant threat; moderate internet skills are not necessarily indicative of high phishing awareness; and direct experiences with phishing do not always correlate with an increased awareness of its potential impact. These results highlight a disconnect between knowledge and perceived risk and suggest that existing internet skills and experiences are insufficient for cultivating a robust understanding of phishing risks. The study underscores the necessity for targeted educational interventions specifically designed to address the varied needs of university staff, enhancing their ability to recognize and respond to cybersecurity threats effectively.