Psychological profiling of hackers via machine learning toward sustainable cybersecurity - Hani, Umema; Sohaib, Osama; Khan, Khalid; Aleidi, Asma; Islam, Noman (Frontiers in Computer Science, 2024)
This research addresses a challenge of the hacker classification framework based on the ‘big five 12 personality traits’ model (OCEAN) and explores associations between personality traits and hacker 13 types. The method's application prediction performance was evaluated on two groups: Students with 14 years of hacking experience intending to pursue information security and ethical hacking and industry 15 professionals working as White Hat hackers. These professionals were further categorized based on 16 of their behavioral tendencies, incorporating Grey Hat traits. The k-means algorithm analyzed intra-17 cluster dependencies, elucidating variations within different clusters and their correlation with Hat 18 types. The study achieved an 88% accuracy in mapping clusters with Hat types, effectively identifying 19 cyber-criminal behaviors. Ethical considerations regarding privacy and bias in personality profiling 20 methodologies within cybersecurity are discussed, emphasizing the importance of informed consent, 21 transparency, and accountability in data management practices. Furthermore, the research underscores 22 the need for sustainable cybersecurity practices, integrating environmental and societal impacts into 23 security frameworks. This study aims to advance responsible cybersecurity practices by promoting 24 awareness and ethical considerations, prioritizing privacy, equity, and sustainability principles.