基于加速度传感器的WLALCS身份认证及实现

Accelerometer Based Authentication Method in WLALCS

针对当前移动设备身份认证方法或易破解、或难实现、或成本高的问题,提出一种新的基于手机加速度传感器的人体感知身份认证方法.该方法利用当前手机普遍内置的加速度传感器采集人体运动数据(通常为动态手势),结合经典匹配算法LCS,提出限制匹配窗口的近似判等最长公共子序列算法,对采样点序列限制区间匹配,针对浮点数据对采样点距离近似判等,进行数据匹配实现身份认证,并基于云计算模型实现了手机身份认证平台.较之已有的基于手势身份认证方法,有效降低了针对模仿动作攻击的接受错误率,非攻击认证相等错误率为2%,而模仿动作攻击的相等错误率降低至5%.该系统具有易于在各类移动设备系统部署,不需要额外的设备等优势,且基于生物特征原理,特别加强了抵抗模仿动作攻击的健壮性,不易被破解.

Aiming at problems in authentication methods of mobile devices, such as being easy to crack or difficult to implement or high costs, this paper presented a new mobile phone acceleration sensor authentication method on human perception. To use the current widespread acceleration sensor in mobile phones to capture human motion data （typically dynamic gesture）, we proposed an authentication algorithm named Window Limited Approximate Longest Common Sequence （WLALCS） based on the classical matching algorithm LCS. And we implemented an authentication system on cloud computing model. Compared with the existing gesture and accelerometer based authentication methods, this method effectively reduced the equal error rate on imitate action attack. Non-imitate attack authentication EER （Equal Error Rate） is 2%, and imitate attack authentication EER is 5%. This system is easy to deploy on any smart phone systems and does not need any additional sensors. Based on the biological characteristics, we reinforced the robustness towards mimicry attack.