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

通信学报 ›› 2018, Vol. 39 ›› Issue (9): 147-158.doi: 10.11959/j.issn.1000-436x.2018163

基于检查点场景信息的软件行为可信预测模型

田俊峰^1,²,郭玉慧^1,²

¹ 河北大学网络空间安全与计算机学院，河北保定 071002
² 河北省高可信信息系统重点实验室，河北保定 071002

修回日期:2018-07-30 出版日期:2018-09-01 发布日期:2018-10-19
作者简介:田俊峰（1965-），男，河北保定人，河北大学教授、博士生导师，主要研究方向为信息安全与分布式计算。|郭玉慧（1992-），女，山西朔州人，河北大学硕士生，主要研究方向为信息安全与分布式计算。
基金资助:
国家自然科学基金资助项目(61170254);河北省自然科学基金资助项目(F2016201244)

Software behavior trust forecast model based on check point scene information

Junfeng TIAN^1,²,Yuhui GUO^1,²

¹ School of Cyber Security and Computer,Hebei University,Baoding 071002,China
² Key Lab on High Trusted Information System in Hebei Province,Baoding 071002,China

Revised:2018-07-30 Online:2018-09-01 Published:2018-10-19
Supported by:
The National Natural Science Foundation of China(61170254);The Natural Science Foundation of Hebei Province(F2016201244)

摘要/Abstract

摘要：

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,radial basis function）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。

关键词: 软件可信性, 检查点, RBF神经网络, 半加权马尔可夫模型

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

中图分类号:

TP393.08

田俊峰,郭玉慧. 基于检查点场景信息的软件行为可信预测模型[J]. 通信学报, 2018, 39(9): 147-158.

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

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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

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

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