Social engineering attacks pose significant cybersecurity threats, exploiting human vulnerabilities instead of relying solely on technical flaws. This systematic literature review aims to examine hybrid intelligence frameworks that leverage advanced technologies alongside human factors to better detect and mitigate these attacks in dynamic environments. Following the PRISMA guidelines, we identified and analyzed relevant studies published between 2016 and 2024. The review categorizes the literature into three primary themes: integration of technology and adaptive measures in detection methodologies, the impact of user behavior on vulnerability, and proposed frameworks or models for both prevention and response strategies.

Key findings indicate that hybrid models incorporating machine learning and behavioral analysis significantly enhance detection capabilities, paving the way for proactive mitigative actions. Mouton et al. outlined detection models applicable to social engineering, emphasizing the need for ongoing adaptation in security frameworks [1]. [2]. highlighted the critical role of user awareness and behavior in mitigating risks, advocating for a psychologically informed approach to social engineering defense [3] . Recent approaches leveraging generative AI illustrate the evolving landscape of social engineering, showcasing automated and highly targeted attacks [4].

Overall, this review identifies significant gaps in traditional defensive mechanisms, underlining the necessity for a multidisciplinary perspective that combines behavioral and technological insights to enhance resilience against social engineering threats. This study lays the groundwork for future inquiries into hybrid frameworks, aiming for a more comprehensive understanding of their roles in cyber security.

Keywords: Hybrid, Intelligence, Framework, Detecting, Mitigating, Social Engineering