Your Email Address Holds the Key: Understanding the Connection Between Email and Password Security with Deep Learning

Etienne Salimbeni, Nina Mainusch, Dario Pasquini
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Abstract

In this work, we investigate the effectiveness of deep-learning-based password guessing models for targeted attacks on human-chosen passwords. In recent years, service providers have increased the level of security of users' passwords. This is done by requiring more complex password generation patterns and by using computationally expensive hash functions. For the attackers this means a reduced number of available guessing attempts, which introduces the necessity to target their guess by exploiting a victim's publicly available information. In this work, we introduce a context-aware password guessing model that better capture attackers' behavior. We demonstrate that knowing a victim's email address is already critical in compromising the associated password and provide an in-depth analysis of the relationship between them. We also show the potential of such models to identify clusters of users based on their password generation behaviour, which can spot fake profiles and populations more vulnerable to context-aware guesses. The code is publicly available at https://github.com/spring-epfl/DCM_sp.
你的电子邮件地址掌握着关键:用深度学习理解电子邮件和密码安全之间的联系
在这项工作中,我们研究了基于深度学习的密码猜测模型对人为选择的密码进行针对性攻击的有效性。近年来,服务提供商提高了用户密码的安全级别。这需要更复杂的密码生成模式和使用计算成本较高的散列函数。对于攻击者来说,这意味着可用的猜测尝试次数减少,这就引入了通过利用受害者的公开信息来瞄准他们的猜测的必要性。在这项工作中,我们引入了一个上下文感知密码猜测模型,可以更好地捕获攻击者的行为。我们证明,了解受害者的电子邮件地址对于泄露相关密码已经至关重要,并提供了对它们之间关系的深入分析。我们还展示了这些模型的潜力,可以根据用户的密码生成行为来识别用户群,这可以发现虚假的个人资料和更容易受到上下文感知猜测的人群。该代码可在https://github.com/spring-epfl/DCM_sp上公开获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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