Social Engineering is a psychological means of manipulating human weakness to steal sensitive data / information for the purpose of harm or theft. Criminals are now resorting to more fashionable social engineering attacks that take advantage of the weakest link in the chain which is humans as a result of increased technological defense. Despite the negative effects, it has been claimed that the attacks' alleged influence on people has gone unnoticed. In order to examine both the attacker's and the victims' perspectives, this paper aimed to ascertain the attacker's viewpoint, motivation, and mechanism. Mixed methodology was used for this research using qualitative and quantitative methods. Questionnaire was used for the quantitative method, while interview was also conducted to obtain further information. Fourteen (14) persons were randomly selected and used as our case study for this research. The result suggests a gender bias in the attacker and the victim, with the males being the predominant attacker with the females being the victim. The result also showed that despite the attackers knowing the consequences of their actions continue in their trade. It was further observed that despite the attacker’s being significantly educated, it was not sufficient to dissuade them from committing crime motivated for illegal gains with security and other consequences. A framework was then proposed to mitigate the actions of social engineering using a social engineering machine learning evaluator mode.
