An approach for mitigating cognitive load in password management by integrating QR codes and steganography

IF 1.5 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Security and Privacy Pub Date : 2024-07-16 DOI:10.1002/spy2.447
G. Balayogi, Kuppusamy K. S.
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引用次数: 0

Abstract

The proliferation of digital services and the imperative for secure authentication have necessitated the management of an expanding array of passwords, imposing a significant cognitive burden on users. The predominant method for authentication remains the use of passwords. However, a critical issue with this approach is that individuals frequently forget their passwords, particularly when managing multiple accounts. This often results in users creating similar or easily guessable passwords for different accounts or writing them down, compromising security. This article investigates an innovative method to mitigate cognitive burden using steganography‐embedded quick response (QR) codes for streamlined password management. The proposed model, named MASTER (Multi‐device‐based Authentication using STEgged QR Codes), was evaluated for usability using the system usability scale (SUS) and the subjective mental effort scale. The security of the model is evaluated using attack analysis and comparative analysis with image visibility and robustness. The evaluation results indicate that the MASTER model achieved a SUS score of 75.94, with the majority of participants agreeing that the system reduces cognitive effort.
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结合二维码和隐写术减轻密码管理认知负荷的方法
随着数字服务的激增和安全认证的要求,有必要对越来越多的密码进行管理,这给用户带来了巨大的认知负担。主要的身份验证方法仍然是使用密码。然而,这种方法的一个关键问题是个人经常忘记密码,尤其是在管理多个账户时。这往往会导致用户为不同的账户创建相似或容易猜到的密码,或者把密码写下来,从而影响安全性。本文研究了一种创新方法,利用隐写术嵌入快速反应(QR)代码来减轻认知负担,从而简化密码管理。所提出的模型名为 MASTER(使用 STEgged QR 码的基于多设备的身份验证),使用系统可用性量表(SUS)和主观脑力量表对其可用性进行了评估。通过攻击分析以及与图像可见性和稳健性的比较分析,对该模型的安全性进行了评估。评估结果表明,MASTER 模型的 SUS 得分为 75.94,大多数参与者都认为该系统减少了认知努力。
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发文量
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