隐式鉴别机制在解决移动智能设备的安全性与易用性冲突方面具有重要而独特的作用。然而，已有工作通常基于单一特征或动作进行隐式鉴别，仅适合于特定动作、场景和范围。为了解决此问题，本文利用用户使用设备时存在位置、环境、状态、生物和行为特征，提出了一种基于多特征融合的隐式鉴别方案。该方案采集设备内置传感器、生物和行为数据，通过支持向量机方法训练和提取特征，设计多特征融合模型和构建隐式鉴别框架，计算用户身份信任水平，设计差异化安全策略并持续透明地鉴别用户身份。实验验证了该方案的有效性，并且能够平衡安全性与易用性和资源消耗。%Implicit authentication mechanism plays an important and unique role in addressing the collision between security and usability on intelligent mobile device.However,most of current studies are usually built on a single feature or action,while suitable for a particular action and scenes.To solve this problem,we proposed a multi-feature fusion based on implicit authentication scheme,which uses the unique feature,such as the location,environment,posture,gait,biometric and behavioral,during use device.In the scheme,the data,such as sensors,biological and behavioral data,are collected,multi-features are trained and extracted by using the support vector machine.Then,the multi-feature fusion model is designed,and the framework of implicit authentication is constructed for calculating the user confidence level.At last,personalized security policy is designed,and the scheme authenticates user continuously and transparently.Experimental results validate the effec-tiveness of the proposed scheme and balance the security and usability and energy consumption.
展开▼
