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            <title xml:lang="en">Web User Profiling Based on Browsing Behavior Analysis</title>
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                <forename type="first">Xiao-Xi</forename>
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            <idno type="halRefHtml">&lt;i&gt;10th IFIP International Conference on Digital Forensics (DF)&lt;/i&gt;, Jan 2014, Vienna, Austria. pp.57-71, &lt;a target="_blank" href="https://dx.doi.org/10.1007/978-3-662-44952-3_5"&gt;&amp;#x27E8;10.1007/978-3-662-44952-3_5&amp;#x27E9;&lt;/a&gt;</idno>
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            <note type="commentary">Part 1: Internet Crime Investigations</note>
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                <title xml:lang="en">Web User Profiling Based on Browsing Behavior Analysis</title>
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                  <date type="end">2014-01-10</date>
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                <editor>Sujeet Shenoi</editor>
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              <p>Determining the source of criminal activity requires a reliable means to estimate a criminal’s identity. One way to do this is to use web browsing history to build a profile of an anonymous user. Since an individual’s web use is unique, matching the web use profile to known samples provides a means to identify an unknown user. This paper describes a model for web user profiling and identification. Two aspects of browsing behavior are examined to construct a user profile, the user’s page view number and page view time for each domain. Four weighting models, based on the term frequency and term frequency – inverse document frequency weighting schemes, are proposed and compared. Experiments involving 51 personal computers demonstrate that the profiling model is very effective at identifying web users.</p>
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