LAUSR

Social engineering is an attack on information security for accessing systems or networks. Social engineering attacks occur when victims do not recognize methods, models, and frameworks to prevent them. The current research explains user studies, constructs, evaluation, concepts, frameworks, models, and methods to prevent social engineering attacks. Unfortunately, there is no specific previous research on preventing social engineering attacks that effectively and systematically analyze it. Current prevention methods, models, and frameworks of social engineering attacks include health campaigns, human as security sensor frameworks, user-centric frameworks, and user vulnerability models. The human as a security sensor framework needs guidance that will explore cybersecurity as super-recognizers, likely policing act for a secure system. This paper intends to critically and rigorously review prior literature on the prevention methods, models, and frameworks of social engineering attacks. We conducted a systematic literature review based on Bryman & Bell’s literature review method. From the review, we found the protocol to effectively prevent social engineering attacks, such as health campaigns, the vulnerability of social engineering victims, and co-utile protocol, which can manage information sharing on a social network. We present this systematic literature review to recommend ways to prevent social engineering attacks.