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 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
