Software behavior trust forecast model based on check point scene information

Junfeng TIAN; Yuhui GUO

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

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Software behavior trust forecast model based on check point scene information

Papers | 更新时间：2024-06-05

- Software behavior trust forecast model based on check point scene information
- Journal on Communications Vol. 39, Issue 9, Pages: 147-158(2018)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61170254);The Natural Science Foundation of Hebei Province(F2016201244)
- DOI：10.11959/j.issn.1000-436x.2018163
  CLC： TP393.08
- Online First：2018-09，
  
  Published：25 September 2018
- 稿件说明：
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Junfeng TIAN, Yuhui GUO. Software behavior trust forecast model based on check point scene information[J]. Journal on Communications, 2018, 39(9): 147-158.
DOI：

Junfeng TIAN, Yuhui GUO. Software behavior trust forecast model based on check point scene information[J]. Journal on Communications, 2018, 39(9): 147-158. DOI： 10.11959/j.issn.1000-436x.2018163.

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。

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.

关键词

Keywords

references

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Qiang HAN

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School of Computer Science, Beijing University of Posts and Telecommunications

Key Laboratory of Trustworthy Distributed Computing and Service,Ministry of Education,Beijing University of Posts and Telecommunications

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