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Multi-Factor Authentication to Systems Login

机译:系统登录的多因素身份验证

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Multi-Factor Authentication is an electronic authentication method in which a computer user is granted access to an application or a website only after successfully presenting two or more factors, or pieces of evidence. It is the first step to protect systems against intruders since the traditional log-in methods (username and password) are not completely protected from hackers, since they can guess them easily using tools. Current Systems use additional methods to increase security, such as using two-factor authentication based on a one-time password via mobile or email, or authentication based on biometrics (fingerprint, eye iris or retina, and face recognition) or via token devices. However, these methods require additional hardware equipment with high cost at the level of small and medium companies. This paper proposes a multi-factor authentication system that combines ease of use and low-cost factors. The system does not need any special settings or infrastructure. It relies on graphical passwords, so the user, in registration phase, chooses three images and memorizes them. In the login phase, the user needs only to choose the correct images that he considered during the registration process in a specific order. The proposed system overcomes many different security threats, such as key-loggers, screen capture attack or shoulder surfing. The proposed method was applied to 170 participants, 75% of them are males and 25% are females, classified according to their age, education level, web experience. One-third of them did not have sufficient knowledge about various security threats.
机译:多因素认证是一种电子认证方法,其中计算机用户仅在成功呈现两个或更多个因素或证据后才能访问对应用程序或网站。它是保护系统的第一步,因为传统的登录方法(用户名和密码)没有完全从黑客完全保护,因为它们可以轻松地使用工具来猜测它们。当前系统使用其他方法来提高安全性,例如使用基于一次性密码的双因素认证,或基于生物识别(指纹,眼睛虹膜或视网膜和面部识别)或通过令牌设备进行认证。然而,这些方法需要额外的硬件设备,在中小型公司的水平上具有高成本。本文提出了一种多因素认证系统,可易于使用和低成本因素。系统不需要任何特殊设置或基础架构。它依赖于图形密码,因此用户在注册阶段,选择三个图像并记住它们。在登录阶段,用户仅需要选择以特定顺序在注册过程中考虑的正确图像。建议的系统克服了许多不同的安全威胁,例如键记录器,屏幕捕获攻击或肩部冲浪。该方法适用于170名参与者,其中75%是男性,25%是女性,根据其年龄,教育水平,网络经验分类。其中三分之一对各种安全威胁没有足够的知识。

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