智能手机冒名顶替者检测与行为数据隐私和最低限度的硬件支持
Smartphone Impostor Detection with Behavioral Data Privacy and Minimalist Hardware Support
摘要：
冒名顶替者是指接管智能手机并获取合法用户的机密和私人信息的攻击者。本文提出了一种深度防御机制，利用简单的深度学习算法快速检测冒名顶替者，与使用需要计算多个特征的机器学习算法的最佳现有工作相比，该机制可以实现更好的检测准确性。与以前的工作不同，我们考虑保护用户的行为（传感器）数据的隐私，不将其暴露在智能手机之外。对于这种情况，我们提出了一种基于递归神经网络（RNN）的深度学习算法，该算法仅使用合法用户的传感器数据来学习他/她的正常行为。我们建议使用预测误差分布（PED），以提高检测精度。我们还展示了一个极简的硬件模块，称为SID的智能手机冒名顶替者检测器，可以设计和集成到智能手机的独立冒名顶替者检测。实验结果表明，与其他RNN加速器相比，SID可以以非常低的硬件成本和能耗支持实时冒名顶替者检测。
英文摘要：
Impostors are attackers who take over a smartphone and gain access to the legitimate user's confidential and private information. This paper proposes a defense-in-depth mechanism to detect impostors quickly with simple Deep Learning algorithms, which can achieve better detection accuracy than the best prior work which used Machine Learning algorithms requiring computation of multiple features. Different from previous work, we then consider protecting the privacy of a user's behavioral (sensor) data by not exposing it outside the smartphone. For this scenario, we propose a Recurrent Neural Network (RNN) based Deep Learning algorithm that uses only the legitimate user's sensor data to learn his/her normal behavior. We propose to use Prediction Error Distribution (PED) to enhance the detection accuracy. We also show how a minimalist hardware module, dubbed SID for Smartphone Impostor Detector, can be designed and integrated into smartphones for self-contained impostor detection. Experimental results show that SID can support real-time impostor detection, at a very low hardware cost and energy consumption, compared to other RNN accelerators.
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。
