基于检查点场景信息的软件行为可信预测模型

Software behavior trust forecast model based on check point scene information

为了保证软件可信性,通过动态监测软件行为,对软件在一段时间内运行的可信状态进行评估,提出了一种基于检查点场景信息的软件行为可信预测模型CBSI-TM。该模型通过在软件运行轨迹中设置若干检查点,并引入相邻检查点时间增量和CPU利用率变化量定义场景信息,用以反映相邻检查点场景信息的关系,然后利用径向基函数(RBF,radialbasisfunction)神经网络分类器评估当前检查点的状态来判断软件的可信情况,并运用半加权马尔可夫模型预测下一个检查点的状态,达到对软件未来运行趋势的可信情况的评估。实验结果证明了CBSI-TM模型能够有效地预测软件未来运行趋势的可信情况,并验证了该模型具有更优的合理性和有效性。

In order to ensure the trustworthiness of software,and evaluate the trusted status of the software after running for a period of time by monitoring software behavior dynamically,a software behavior trust forecast model on checkpoint scene information which was called CBSI-TM was presented.The model set up a number of checkpoints in the software running track,and introduced the time increment of adjacent checkpoints,and the change of CPU utilization rate to define the scene information,and reflected the relationship between adjacent checkpoints scene information.Then the RBF neural network classifier evaluated the status of the current checkpoint to judge the trustworthiness of the software,and the semi weighted Markov model predicted the situation of the next checkpoint to evaluate the trustworthiness of future running trend of the software.The experimental results show that the CBSI-TM model can predict the future trusted status of the software effectively,and verify that the model is more reasonable and effective.