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A new approach to modelling the effects of cognitive processing and threat detection on phishing susceptibility

机译:建模认知处理和威胁检测对网络钓鱼敏感性的影响的新方法

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Phishing threats are real and are ever increasing in their reach and devastating effects. This study delves into the role of cognitive processing in detecting and curtailing phishing attacks. The proposed model is grounded on the Elaboration Likelihood Model and is tested empirically using data from 192 cases. Data was collected through direct observations of phishing susceptibility and self-reported questionnaires after staging a phishing attack targeting a university population in Nairobi, Kenya. The model was found to have excellent fit and was able to account for 50.8% of a person's cognitive processing of a phishing attack, 69.5% of their ability to detect phishing threats and could predict 28% of their actual phishing susceptibility. Analysis was done to test 25 hypothesis, and to examine the mediating effects of cognitive processing and threat detection. In addition, multi group moderation analysis was done to examine if the model was invariant based on the level of knowledge. Results indicate that threat detection has the strongest effect in reducing phishing susceptibility. Threat detection was found to be what explains why people who expend cognitive effort processing phishing communication are less likely to fall for phishing threats.
机译:网络钓鱼威胁是真实存在的,并且其影响范围和破坏力正在不断增加。这项研究探讨了认知处理在检测和减少网络钓鱼攻击中的作用。所提出的模型基于细化可能性模型,并使用192个案例的数据进行了经验检验。在针对肯尼亚内罗毕的大学人群进行网络钓鱼攻击后,通过对网络钓鱼敏感性的直接观察和自我报告的调查表收集了数据。该模型具有很好的适应性,可以占一个人对网络钓鱼攻击的认知处理的50.8%,占其检测网络钓鱼威胁的能力的69.5%,并且可以预测其实际网络钓鱼敏感性的28%。进行分析以检验25个假设,并检查认知过程和威胁检测的中介作用。此外,进行了多组审核分析,以基于知识水平检查模型是否不变。结果表明,威胁检测在降低网络钓鱼易感性方面具有最强的作用。发现威胁检测是解释为什么花大量时间在处理网络钓鱼通信上的人们进行网络钓鱼威胁的原因。

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