基于检查点场景信息的软件行为可信预测模型

田俊峰; 郭玉慧

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

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基于检查点场景信息的软件行为可信预测模型

论文Ⅲ：学术论文 | 更新时间：2024-06-05

- 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
- 通信学报 2018年39卷第9期页码：147-158
- 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：
  
  [ "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。" ]
- 基金信息：
  
  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)
- DOI：10.11959/j.issn.1000-436x.2018163
  中图分类号： TP393.08
- 网络出版日期：2018-09，
  
  纸质出版日期：2018-09-25
- 稿件说明：
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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.
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. DOI： 10.11959/j.issn.1000-436x.2018163.

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

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