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This systematic literature review analyzes 34 studies on the integration of artificial intelligence (AI) in cyberpsychology (CPSY) to enhance cybersecurity by understanding the psychological traits of victims, attackers, and defenders. By employing the PRISMA methodology, the review categorizes applications into four main areas: Anomaly Detection, Vulnerability Risk Prediction, Security Awareness Training, and Authentication/Identity Verification, while also detailing the AI techniques utilized, such as machine learning and natural language processing. Key findings highlight prevalent psychological concepts, dataset usage, and identify significant research gaps and challenges in the AI-CPSY domain, paving the way for future advancements.
AI-driven insights into cyberpsychology reveal critical vulnerabilities and opportunities for enhancing cybersecurity strategies through psychological profiling.
Cybersecurity is the practice of protecting systems, networks, and data from digital attacks. Cyberpsychology (CPSY) is defined as the use of psychology to enhance cybersecurity applications. Since the early 2010s, the evolution of Artificial Intelligence (AI) has increasingly integrated with CPSY, leveraging advanced data analysis to decode the distinct personality traits and behavioral patterns of victims, attackers, and defenders. In this systematic literature review (SLR), we carefully analyze 34 collected research studies of AI usage in cyberpsychology (AI-CPSY) using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. The review presents a comprehensive taxonomy of the cyber-security applications, the AI methodologies used, and the psychological concepts employed across the studies . We sort the research studies into four cybersecurity applications: Anomaly Detection (AD), Vulnerability Risk Prediction (VRP), Security Awareness Training (SAT), and Authentication/Identity Verification (AIV). Within each application area, studies are further sorted according to the AI method used including machine learning (ML), deep learning (DL), natural language processing (NLP), and reinforcement learning (RL). Furthermore, the review identifies the most commonly utilized psychological concepts, quantify the datasets used in the field, and present their current implementation and deployment status. At last, it detect research gaps, present open challenges, and deduce the trending and most effective and emerging methodologies used across the AI-CPSY landscape.