一种用于网络取证分析的模糊决策树推理方法

网络取证是对现有网络安全体系的必要扩展,已日益成为研究的重点.但目前在进行网络取证时仍存在很多挑战:如网络产生的海量数据;从已收集数据中提取的证据的可理解性;证据分析方法的有效性等.针对上述问题,利用模糊决策树技术强大的学习能力及其分析结果的易理解性,开发了一种基于模糊决策树的网络取证分析系统,以协助网络取证人员在网络环境下对计算机犯罪事件进行取证分析.给出了该方法的实验结果以及与现有方法的对照分析结果.实验结果表明,该系统可以对大多数网络事件进行识别(平均正确分类率为91.16%),能为网络取证人员提供可理解的信息,协助取证人员进行快速高效的证据分析.

Fuzzy Decision Tree Based Inference Techniques for Network Forensic Analysis

Network forensics is an important extension to present security infrastructure,and is becoming the research focus of forensic investigators and network security researchers.However many challenges still exist in conducting network forensics:The sheer amount of data generated by the network;the comprehensibility of evidences extracted from collected data;the efficiency of evidence analysis methods,etc.Against above challenges,by taking the advantage of both the great learning capability and the comprehensibility of the analyzed results of decision tree technology and fuzzy logic,the researcher develops a fuzzy decision tree based network forensics system to aid an investigator in analyzing computer crime in network environments and automatically extract digital evidence.At the end of the paper,the experimental comparison results between our proposed method and other popular methods are presented.Experimental results show that the system can classify most kinds of events (91.16% correct classification rate on average),provide analyzed and comprehensible information for a forensic expert and automate or semi-automate the process of forensic analysis.