中文摘要：

目前的可信计算机只能保证系统资源的静态安全，而系统开始运行后，终端计算机的可信性主要取决于在其上运行的软件行为的可信性．针对软件运行过程中的动态可信性，在传统软件行为模型的基础上引入了基于区间数据的检查点场景级属性，提出了基于检查点分级属性的软件动态可信评测模型．该模型基于预期行为轨迹进行软件行为的可信评测；采用检查点属性分级策略，对基于阈值的检查点可信评价进行简化，并将检查点属性主观分级赋权与场景级客观赋权相结合；通过建立场景级属性可信模型进行基于区间数据的场景级属性可信评测．理论分析表明基于分级属性的决策方法减少了与阈值的比较次数，实验结果验证了基于区间数据的场景级属性可信评测的有效性以及较高的攻击检测能力．

英文摘要：

The trusted computer can only ensure the static security at present. When the system begins to run, the trustworthiness of terminal computer mainly depends on the trustworthiness of software behavior running on it. Therefore, the dynamic trustworthiness of softv~ore during running has become a critical problem to solve the trustworthiness of computer system. According to the dynamic trustworthiness of software during running, checkpoint＇s scene-level attributes based on interval data are introduced and a dynamic trustworthiness evaluation model of software based on checkpoint＇s classification attributes is presented based on the traditional software behavior model. The model evaluates the trustworthiness of software behavior based on the trace of expected behavior. It uses checkpoint attributes＇ classification strategy which simplifies the trustworthiness evaluation of checkpoints based on threshold, and combines checkpoint attributes＇ subjective classification weighting with scene-level attributes＇ objective weighting. The model evaluates the trustworthiness of scene-level attributes based on interval data by constructing trusted model of scene-level attributes. The theoretical analysis shows that the classification attributes decision making reduces the number of comparisons with the threshold, and the experimental results verify the effectiveness of trustworthiness evaluation of scene-level attributes based on interval data and the high attack detection capability of the dynamic trustworthiness evaluation model.