基于行为足迹的多模态融合身份认证

Identity Authentication of Multi-Modal Fusion Based on Behavioral Footprint

针对单模态身份认证方法存在特征单一容易被伪造和攻破的问题,提出基于用户行为足迹的多模态特征融合隐式身份认证方法。在移动设备中采集用户使用设备时的触摸压力、触摸轨迹、加速度等传感器数据,利用特征选择技术提取触摸屏交互、移动模式、物理位置等特征并对其进行训练与融合,最终通过多模态特征融合模型实现用户身份认证。实验结果表明,该方法采用的特征级融合和决策级融合方式均获得了98%以上的认证准确率,相比单模态身份认证方法更难以被伪造和攻破,且认证准确率更高、稳定性更强。

Among identity verification methods,the single-mode verification methods rely on single features,and are vulnerable to forged authentication and attacks.To solve the problem,an implicit authentication method of multi-modal feature fusion based on user footprints is proposed.The data of user behavior when using the mobile devices,including the touch pressure,the track of finger movement,and the acceleration of user movement,is collected from sensors.Then the feature selection technique is used to extract the features of touch screen interactions,movement mode,and physical location.The extracted features are subsequently trained and fused.On this basis,the multi-modal feature fusion model is used to realize user identity authentication.Experimental results show that the proposed method achieves an authentication accuracy of over 98%in both the feature-level fusion mode and the strategy-level fusion mode.It is less vulnerable to forged authentication and attacks,and displays higher authentication accuracy and stability.