文章基本信息

标题：Propose a Framework for Criminal Mining by Web Structure and Content Mining
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作者：Javad Hosseinkhani ; Suriayati Chuprat ; Hamed Taherdoost 等
期刊名称：International Journal of Advanced Computer Science and Information Technology
电子版ISSN：2296-1739
出版年度：2012
卷号：1
期号：1
页码：13
语种：English
出版社：Helvetic Editions
摘要：Anonymous and suitable information always are provided by criminal web data for Law enforcement agencies. The digital data that are used in forensic analysis comprises of pieces of information which are about the accused social networks. Nevertheless, evaluating these pieces of information is a challenging means an operator has to extract the appropriate information from the text in the website manually, after that it finds a connection between different pieces of information and classify them into a database structure. Then, the set is ready to utilize different criminal network analysis tools for test. Therefore, this manual procedure of arranging data for analysis is not efficient because it affected by many errors. Moreover, the quality of achieving analyzed data is related to the expertise and experience of the investigator so the reliability of the tests is not continuous. Actually, the better result is achived, the more knowledgeable is an operator. The aim of this study is to report the process of exploring the criminal suspects of forensic data analysis that support the reliability gap by offering a framework by utilizing Highlevel architecture of a scalable universal crawler.