AN INTRUSION-TOLERANT SYSTEM STATE TRANSITION MODEL BASED LEARNING AND STABILITY ANALYSIS

摘要：

To improve the adaptability of Intrusion-tolerant systems,an online learning state is added on the general state transition model of Intrusiontolerant.It allows the system to constantly learn the latest network attacks characteristics in order to meet the new network environment.A semi-Markov process model (SMP) is built based on the model.The steady-state probability-of each state in the model is calculated and compared the difference through online learning under four kinds of network attacks.The result shows that the on-line learning state can improve the stability of Intrusion-tolerant systems.

关键词： Intrusion Tolerance Markov Process steady probability

作者: Fang Zhou Xuefeng Zheng Hua Sun

作者单位: School of Information Engineering University of Science and Technology Beijing Beijing,China School of Information Engineering University of Science and Technology BeijingBeijing,China

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Computer and Network Technology(ICCNT 2011)(2011第三届IEEE计算机与网络技术国际会议)

会议地点: 太原

会议语种:英文

页码: 335-338

在线出版日期: 2011-02-26（万方平台首次上网日期，不代表论文的发表时间）