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Susceptibility to phishing on social network sites: A personality information processing model

机译:对社交网站上网络钓鱼的易感性:一个人格信息处理模型

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Today, the traditional approach used to conduct phishing attacks through email and spoofed websites has evolved to include social network sites (SNSs). This is because phishers are able to use similar methods to entice social network users to click on malicious links masquerading as fake news, controversial videos and other opportunities thought to be attractive or beneficial to the victim. SNSs are a phisher's "market" as they offer phishers a wide range of targets and take advantage of opportunities that exploit the behavioural vulnerabilities of their users. As such, it is important to further investigate aspects affecting behaviour when users are presented with phishing. Based on the literature studied, this research presents a theoretical model to address phishing susceptibility on SNSs. Using data collected from 215 respondents, the study examined the mediating role that information processing plays with regard to user susceptibility to social network phishing based on their personality traits, thereby identifying user characteristics that may be more susceptible than others to phishing on SNSs. The results from the structural equation modeling (SEM) analysis revealed that conscientious users were found to have a negative influence on heuristic processing, and are thus less susceptible to phishing on SNSs. The study also confirmed that heuristic processing increases susceptibility to phishing, thus supporting prior studies in this area. This research contributes to the information security discipline as it is one of the first to examine the effect of the relationship between the Big Five personality model and the heuristic-systematic model of information processing.
机译:今天,通过电子邮件和欺骗网站进行网络钓鱼攻击的传统方法已经发展到包括社交网站(SNSS)。这是因为Phishers能够使用类似的方法来吸引社交网络用户点击伪装成假新闻,有争议的视频和其他机会对受害者有吸引力或有利于受害者的恶意链接。 SNSS是一个Phisher的“市场”,因为它们提供了广泛的目标,并利用利用其用户的行为漏洞的机会。因此,重要的是进一步研究当用户呈现网络钓鱼时影响行为的方面。基于研究的研究,该研究提出了一种解决SNSS上网络钓鱼敏感性的理论模型。研究从215名受访者中收集的数据,研究了根据其个性特征,在用户对社交网络钓鱼的用户易感性方面发挥的调解作用,从而识别用户特征,这些特征可能比其他人更容易受到SNS的网络钓鱼。结构方程模型(SEM)分析的结果显示,发现认可的用户对启发式处理产生负面影响,因此对SNSS的钓鱼较小。该研究还证实,启发式处理增加了对网络钓鱼的易感性,从而支持该地区的先前研究。这项研究有助于信息安全纪律,因为它是第一个检查大五个人格模型与信息处理的启发式系统模型之间关系的效果之一。

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