arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1703.03378cs.CR
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多传感器身份验证,提高智能手机安全性

Multi-sensor authentication to improve smartphone security

Wei-Han Lee, Ruby Lee

摘要:

智能手机的广泛使用引起了新的安全和隐私问题。在最近的犯罪统计数据中,智能手机盗窃占盗窃案的比例最大。使用受害者的智能手机,攻击者可以发起模拟攻击,威胁受害者和网络中其他用户的安全。我们的威胁模型包括攻击者在用户使用密码或PIN登录后接管手机。我们的目标是为智能手机设计一种机制,以更好地持续和隐式地验证当前用户,并在必要时发出警报。在本文中,我们提出了一个基于多传感器的系统,以实现连续和隐式认证的智能手机用户。该系统不断学习所有者的行为模式和环境特征,然后在不中断用户与智能手机交互的情况下对当前用户进行身份验证。我们的方法可以自适应地更新用户的模型,考虑到用户的模式的时间变化。实验结果表明,该方法是有效的,训练模型所需时间不到10秒,检测异常用户所需时间不到20秒,同时达到了较高的准确率(90%以上)。此外,更多传感器的组合提供了更好的精度。此外,我们的方法能够通过改变采样率来调整安全级别。

英文摘要:

The widespread use of smartphones gives rise to new security and privacy concerns. Smartphone thefts account for the largest percentage of thefts in recent crime statistics. Using a victim's smartphone, the attacker can launch impersonation attacks, which threaten the security of the victim and other users in the network. Our threat model includes the attacker taking over the phone after the user has logged on with his password or pin. Our goal is to design a mechanism for smartphones to better authenticate the current user, continuously and implicitly, and raise alerts when necessary. In this paper, we propose a multi-sensors-based system to achieve continuous and implicit authentication for smartphone users. The system continuously learns the owner's behavior patterns and environment characteristics, and then authenticates the current user without interrupting user-smartphone interactions. Our method can adaptively update a user's model considering the temporal change of user's patterns. Experimental results show that our method is efficient, requiring less than 10 seconds to train the model and 20 seconds to detect the abnormal user, while achieving high accuracy (more than 90%). Also the combination of more sensors provide better accuracy. Furthermore, our method enables adjusting the security level by changing the sampling rate.

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。

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