基于检查点场景信息的软件行为可信预测模型

doi:10.11959/j.issn.1000-436x.2018163

Abstract

Abstract:

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.

Key words: software trustworthiness, check point, RBF neural network, semi weighted Markov model

CLC Number:

TP393.08

Junfeng TIAN,Yuhui GUO. Software behavior trust forecast model based on check point scene information[J]. Journal on Communications, 2018, 39(9): 147-158.

Figures/Tables 11

References 21

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标度	定义	说明
Ⅰ	同等重要	2个场景信息因素的重要性相当
Ⅱ	稍微重要	2个场景信息因素的重要性差不多
Ⅲ	明显重要	2个场景信息因素的重要性差异相对显著
Ⅳ	重要得多	2个场景信息因素的重要性差异非常明显
Ⅴ	极端重要	2个场景信息因素的重要性处于2个极端
Ⅵ	反比较	若因素a_i与a_j比较得到判断r_ij，则r_ji=1-r_ij

类别	C₁	C₂	C₃	总计	正确率
C₁	90	0	0	90	100%
C₂	0	86	4	90	95.56%
C₃	0	7	83	90	92.22%
总计	90	93	87	270	95.93%

类别	C₁	C₂	C₃	总计	正确率
C₁	90	0	0	90	100%
C₂	0	82	8	90	91.11%
C₃	0	6	84	90	93.33%
总计	90	88	92	270	94.81%

标度	取值
Ⅰ	0.5
Ⅱ	0.6
Ⅲ	0.7
Ⅳ	0.8
Ⅴ	0.9
Ⅵ	0.1,0.2,0.3,0.4

Software behavior trust forecast model based on check point scene information

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