Nowadays, Passwords are to identify users of Social Networking Sites (SNS). However, there are some downsides to it such as the user forgetting their password or the account being hacked by an attacker. To retrieve their password, websites are asking for an alternate email address or security question. Due to its expanding features and popularity, social media has become more vulnerable. It is no secret that social media providers leave security to the discretion of users during the development, which raises serious concerns. Earlier research has been supported by the Protective Motivation Theory (PMT), which provides a hypothetical structure for analyzing the protection of Internet users. According to the current internet safety search, new engines incorporated into a PMT structure for perhaps the first time. Adaptation assessment variables, including the strength of behavior, the effectiveness of responses, and individual responsibility, were the most important predictors of online safety intentions. A multi-factor authentication mechanism linked to a trust model used on SNS. The needs, actions, and behaviors of users are particularly adaptable to confidence systems. The level of threat was also a significant predictor. The explanatory power of the standard PMT model with the multi-factor authentication technique was raised by 15% by incorporating new components such as past experiences, behavioral control, habitual strength, security and safety support, and individual responsibility. The results are considered in the advanced evolution of PMT in the context of Internet security for home computer users.

中文翻译：

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使用多因素身份验证提供社交网站安全性的保护动机理论


如今，密码用于识别社交网站（SNS）的用户。然而，它也有一些缺点，例如用户忘记密码或帐户被攻击者黑客攻击。为了找回密码，网站要求提供备用电子邮件地址或安全问题。由于其不断扩大的功能和受欢迎程度，社交媒体变得更加脆弱。社交媒体提供商在开发过程中将安全性留给用户自行决定，这已不是什么秘密，这引起了严重的担忧。早期的研究得到了保护动机理论（PMT）的支持，该理论为分析互联网用户的保护提供了一个假设结构。根据当前的互联网安全搜索，新引擎可能首次融入 PMT 结构。适应评估变量，包括行为强度、响应有效性和个人责任，是在线安全意图最重要的预测因素。与 SNS 上使用的信任模型相关联的多因素身份验证机制。用户的需求、行动和行为特别适合信任系统。威胁程度也是一个重要的预测因素。通过纳入过去经验、行为控制、习惯强度、安全保障支持、个人责任等新成分，采用多因素认证技术的标准PMT模型的解释力提高了15%。在家庭计算机用户互联网安全的背景下，PMT 的高级发展考虑了这些结果。