基于神经网络的安全风险概率预测模型  

Risk Probability Estimating Model Based on Neural Networks

作　　者：刘芳[1] 蔡志平[1] 肖侬[1] 王志英[1] 陈勇[2] 

机构地区：[1]国防科技大学计算机学院,长沙410073 [2]都柏林大学圣三一学院计算机系,爱尔兰都柏林

出　　处：《计算机科学》2008年第12期28-33,共6页Computer Science

基　　金：国家自然科学基金(No.90104025,No.60603062);国家重点基础研究发展计划(973)(No.2007CB310901);湖南省自然科学基金(No.06JJ3035)

摘　　要：网络安全风险概率预测对分布式网络环境及其内在的不确定事件进行动态分析和评价,是构建网络安全保障体系的重要环节。深入研究网络态势感知中的特征提取、聚类分析、相似性度量和预测方法,提出了一种基于神经网络的安全风险概率预测模型。采用入侵检测数据进行了实例验证,仿真实验结果验证了风险预测方法的可行性与有效性。Risk probability estimating analyzes the distributed network and assesses lnherenUy uncertain events and circumstances dynamically. And the estimating probability of security risk is an absolutely necessary step of developing network security protection system. The problems of feature extraction, cluster analysis, similarity measurement and estimation methods in network situation awareness were addressed. A risk probability assessment formula was proposed, and an estimating model adopting the neural networks was presented. An experiment based on DARPA intrusion detection evaluation data was given to support the suggested approach and demonstrated the feasibility and suitability for use.

关 键 词：网络安全 风险概率 预测模型 神经网络 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术] P315.9[自动化与计算机技术—计算机科学与技术]