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This paper discusses the challenges of performing a forensic investigation against a multi-node Hadoop cluster and proposes a methodology for examiners to use in such situations. The procedure's aim of minimising disruption to the data centre during the acquisition process is achieved through the use of RAM forensics. This affords initial cluster reconnaissance which in turn facilitates targeted data acquisition on the identified DataNodes. To evaluate the methodology's feasibility, a small Hadoop Distributed File System (HDFS) was configured and forensic artefacts simulated upon it by deleting data originally stored in the cluster. RAM acquisition and analysis was then performed on the NameNode in order to test the validity of the suggested methodology. The results are cautiously positive in establishing that RAM analysis of the NameNode can be used to pinpoint the data blocks affected by the attack, allowing a targeted approach to the acquisition of data from the DataNodes, provided that the physical locations can be determined. A full forensic analysis of the DataNodes was beyond the scope of this project.  相似文献   
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基于分布式计算的网络舆情分析系统的设计   总被引:1,自引:0,他引:1  
本文提出了一种基于分布式计算的网络舆情分析系统,通过利用Hadoop的分布式存储和计算框架,基于K-means和Canopy两种聚类算法,设计了MapReduce聚类算法,支持了高性能的并行计算,实现了高效的舆情分析和预警。  相似文献   
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