Boosting the Guessing Attack Performance on Android Lock Patterns with Smudge Attacks

Seunghun Cha, Sungsu Kwag, Hyoungshick Kim, J. Huh
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引用次数: 41

Abstract

Android allows 20 consecutive fail attempts on unlocking a device. This makes it difficult for pure guessing attacks to crack user patterns on a stolen device before it permanently locks itself. We investigate the effectiveness of combining Markov model-based guessing attacks with smudge attacks on unlocking Android devices within 20 attempts. Detected smudges are used to pre-compute all the possible segments and patterns, significantly reducing the pattern space that needs to be brute-forced. Our Markov-model was trained using 70% of a real-world pattern dataset that consists of 312 patterns. We recruited 12 participants to draw the remaining 30% on Samsung Galaxy S4, and used smudges they left behind to analyze the performance of the combined attack. Our results show that this combined method can significantly improve the performance of pure guessing attacks, cracking 74.17% of patterns compared to just 13.33% when the Markov model-based guessing attack was performed alone---those results were collected from a naive usage scenario where the participants were merely asked to unlock a given device. Even under a more complex scenario that asked the participants to use the Facebook app for a few minutes---obscuring smudges were added as a result---our combined attack, at 31.94%, still outperformed the pure guessing attack at 13.33%. Obscuring smudges can significantly affect the performance of smudge-based attacks. Based on this finding, we recommend that a mitigation technique should be designed to help users add obscurity, e.g., by asking users to draw a second random pattern upon unlocking a device.
通过涂抹攻击提高Android锁定模式的猜测攻击性能
安卓允许连续20次解锁失败。这使得纯猜测攻击很难在被盗设备永久锁定之前破解其用户模式。我们研究了将基于马尔可夫模型的猜测攻击与涂抹攻击相结合,在20次尝试内解锁Android设备的有效性。检测到的污迹被用来预先计算所有可能的片段和模式,大大减少了需要暴力强迫的模式空间。我们的马尔可夫模型是使用由312个模式组成的真实世界模式数据集的70%进行训练的。我们招募了12名参与者,让他们在三星Galaxy S4上绘制剩余的30%,并使用他们留下的污迹来分析联合攻击的性能。我们的研究结果表明,这种组合方法可以显著提高纯猜测攻击的性能,破解74.17%的模式,而单独执行基于马尔可夫模型的猜测攻击时,破解率仅为13.33%——这些结果来自一个简单的使用场景,参与者只被要求解锁给定的设备。即使在一个更复杂的场景下,要求参与者使用Facebook应用程序几分钟——结果是添加了模糊的污物——我们的联合攻击的成功率为31.94%,仍然超过了纯粹猜测攻击的13.33%。模糊污迹会显著影响基于污迹的攻击的性能。基于这一发现,我们建议设计一种缓解技术来帮助用户增加模糊性,例如,要求用户在解锁设备时绘制第二个随机图案。
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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