Entry Ding:2015:VED from tissec.bib

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BibTeX entry

@Article{Ding:2015:VED,
  author =       "Steven H. H. Ding and Benjamin C. M. Fung and Mourad
                 Debbabi",
  title =        "A Visualizable Evidence-Driven Approach for Authorship
                 Attribution",
  journal =      j-TISSEC,
  volume =       "17",
  number =       "3",
  pages =        "12:1--12:??",
  month =        mar,
  year =         "2015",
  CODEN =        "ATISBQ",
  DOI =          "https://doi.org/10.1145/2699910",
  ISSN =         "1094-9224 (print), 1557-7406 (electronic)",
  ISSN-L =       "1094-9224",
  bibdate =      "Fri Mar 27 17:03:46 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tissec.bib",
  abstract =     "The Internet provides an ideal anonymous channel for
                 concealing computer-mediated malicious activities, as
                 the network-based origins of critical electronic
                 textual evidence (e.g., emails, blogs, forum posts,
                 chat logs, etc.) can be easily repudiated. Authorship
                 attribution is the study of identifying the actual
                 author of the given anonymous documents based on the
                 text itself, and for decades, many linguistic
                 stylometry and computational techniques have been
                 extensively studied for this purpose. However, most of
                 the previous research emphasizes promoting the
                 authorship attribution accuracy, and few works have
                 been done for the purpose of constructing and
                 visualizing the evidential traits. In addition, these
                 sophisticated techniques are difficult for cyber
                 investigators or linguistic experts to interpret. In
                 this article, based on the End-to-End Digital
                 Investigation (EEDI) framework, we propose a
                 visualizable evidence-driven approach, namely VEA,
                 which aims at facilitating the work of cyber
                 investigation. Our comprehensive controlled experiment
                 and the stratified experiment on the real-life Enron
                 email dataset demonstrate that our approach can achieve
                 even higher accuracy than traditional methods;
                 meanwhile, its output can be easily visualized and
                 interpreted as evidential traits. In addition to
                 identifying the most plausible author of a given text,
                 our approach also estimates the confidence for the
                 predicted result based on a given identification
                 context and presents visualizable linguistic evidence
                 for each candidate.",
  acknowledgement = ack-nhfb,
  articleno =    "12",
  fjournal =     "ACM Transactions on Information and System Security",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J789",
}

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