论文标题
有条件的生成对抗网络，用于击键介绍攻击
Conditional Generative Adversarial Network for keystroke presentation attack
论文作者
论文摘要
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。从这个意义上讲，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。但是，这些传统的身份验证方法不足以确保数据保护，因此，行为生物识别技术已变得重要。尽管它们的结果和广泛应用，但生物识别系统已证明很容易受到恶意攻击的影响，例如演示攻击。因此，在这项工作中，我们建议研究一种旨在将演示攻击部署到击键身份验证系统的新方法。我们的想法是使用有条件的生成对抗网络（CGAN）生成可用于模拟授权用户的合成关键数据。这些综合数据是在两个不同的实际用例之后生成的，一种是已知键入单词的顺序（动态有序），而该顺序未知（无定分的动态）。最后，使用外部按键身份验证系统验证了两个击键动力学（有序和未订购）。结果表明，CGAN可以有效地生成可用于欺骗击键身份验证系统的击键动力学模式。
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.
