A Trustworthy Behavior Model for Software Monitoring Point Based on Classification Attributes

In order to estimate the software trustworthiness accurately, a trustworthy behavior model for software monitoring point based on classification attributes is proposed for the software monitoring point in the expected behavior trace of software. Firstly, the attributes of software monitoring point are classified according to the sphere of action during the trustworthiness evaluation and the trustworthy behavior model of each attribute level is constructed. Secondly, for scene level attributes, a clustering algorithm of scene level attributes based on Gaussian kernel function is presented considering the distinction of training samples of one monitoring point, and a weight distribution strategy for scene level attributes based on one-class samples is proposed for one-class training samples. Finally, experiments and analyses show that: the model can evaluate software monitoring point accurately; For trustworthy behavior model of scene level attributes, the clustering algorithm has lower classification error rate than other clustering algorithms, and the weight distribution strategy has better effect of trustworthiness evaluation than other methods of weight distribution for one-class samples.