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计算机工程 ›› 2021, Vol. 47 ›› Issue (10): 132-139,146. doi: 10.19678/j.issn.1000-3428.0060480

• 网络空间安全 • 上一篇    下一篇

基于动态信任值的智能手机隐式认证方案

张彭明, 张晓梅, 胡建鹏   

  1. 上海工程技术大学 电子电气工程学院, 上海 201620
  • 收稿日期:2021-01-04 修回日期:2021-02-27 发布日期:2021-02-08
  • 作者简介:张彭明(1993-),男,硕士研究生,主研方向为移动设备隐式认证;张晓梅(通信作者)、副教授;胡建鹏,副教授。
  • 基金资助:
    国家自然科学基金(61802252)。

Implicit Authentication Scheme for Smart Phone Based on Dynamic Trust Value

ZHANG Pengming, ZHANG Xiaomei, HU Jianpeng   

  1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
  • Received:2021-01-04 Revised:2021-02-27 Published:2021-02-08

摘要: 在智能手机隐私安全领域,隐式认证具有高安全性、友好交互体验等优点,但存在行为特征采集不便、认证模型复杂的问题。提出一种基于动态信任值的分级隐式认证方案。利用机器学习方法进行模型训练,提取用户划屏行为特征作为前级认证数据,并将前级输出概率经信任值检测作为后级认证数据,进而得到最终认证结果。同时基于真实用户历史认证变化的稳定性和连续性,通过计算一定时间窗口内的认证概率均值作为动态信任更新值,使信任值在真实用户认证结果变化范围内波动。实验结果表明,该方案的分类准确率达到98.63%,等错误率仅为3.43%,与只包含前级认证的方案相比准确性更高,并且能够有效阻挡冒名者非法使用手机。

关键词: 隐私安全, 隐式认证, 行为特征, 机器学习, 动态信任值

Abstract: In the field of privacy and security of smart phones, implicit authentication has been widely studied as it can provide high security and user-friendly interactions.Still, the existing implicit authentication schemes suffer from difficulty in behavior feature collection and high complexity of authentication models.Given the limitations, a hierarchical implicit authentication scheme is proposed based on dynamic trust value.The scheme employs machine learning algorithms to train the model, extracting the features of the scrolling behavior as front-level authentication data.The output probability receives a trust value detection, and the result is taken as the input of back-level authentication data to output the final authentication result.Based on the stability and continuity of real user history authentication, this scheme calculates the average authentication probability value in a certain time window as the dynamically updated trust value, making the trust value ranging within the real user's authentication results.The experimental results demonstrate that the proposed scheme can achieve a classification accuracy of 98.63% and an equal error rate of 3.43%.Compared to the methods with only front-level authentication scheme, the proposed scheme can improve the accuracy of authentication and effectively prevent the impostors from illegally using the phones.

Key words: privacy and security, implicit authentication, behavior features, machine learning, dynamic trust value

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