基于条件生成对抗网络的随机呈现攻击
Conditional Generative Adversarial Network for keystroke presentation attack
摘要：
网络安全是数据保护的关键一步，以确保用户安全和个人数据隐私。从这个意义上说，许多公司已经开始使用身份验证系统来控制和限制对其数据的访问。然而，这些传统的身份验证方法不足以确保数据保护，并且由于这个原因，行为生物识别变得重要。尽管生物特征识别系统具有良好的应用前景和广泛的应用范围，但它仍然容易受到恶意攻击，例如呈现攻击。出于这个原因，在这项工作中，我们建议研究一种新的方法，旨在部署一个演示攻击的身份验证系统。我们的想法是使用条件生成对抗网络（cGAN）来生成可用于冒充授权用户的合成对抗数据。这些合成数据是在两个不同的实际用例之后生成的，其中一个用例中键入的单词的顺序是已知的（有序动态），另一个用例中该顺序是未知的（无序动态）。最后，这两个动态（有序和无序）验证使用外部认证系统。结果表明，cGAN可以有效地生成可用于欺骗身份验证系统的动态模式。
英文摘要：
Cybersecurity is a crucial step in data protection to ensure user security and personal data privacy. In this sense, many companies have started to control and restrict access to their data using authentication systems. However, these traditional authentication methods, are not enough for ensuring data protection, and for this reason, behavioral biometrics have gained importance. Despite their promising results and the wide range of applications, biometric systems have shown to be vulnerable to malicious attacks, such as Presentation Attacks. For this reason, in this work, we propose to study a new approach aiming to deploy a presentation attack towards a keystroke authentication system. Our idea is to use Conditional Generative Adversarial Networks (cGAN) for generating synthetic keystroke data that can be used for impersonating an authorized user. These synthetic data are generated following two different real use cases, one in which the order of the typed words is known (ordered dynamic) and the other in which this order is unknown (no-ordered dynamic). Finally, both keystroke dynamics (ordered and no-ordered) are validated using an external keystroke authentication system. Results indicate that the cGAN can effectively generate keystroke dynamics patterns that can be used for deceiving keystroke authentication systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
