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Applications and the Internet (SAINT), 2011 IEEE/IPSJ 11th International Symposium on

Date 18-21 July 2011

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Displaying Results 1 - 25 of 126
  • [Front cover]

    Page(s): C1
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  • [Title page i]

    Page(s): i
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  • [Title page iii]

    Page(s): iii
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  • [Copyright notice]

    Page(s): iv
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  • Table of contents

    Page(s): v - xvi
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  • Message from the General Chairs

    Page(s): xvii
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  • Message from the Program Chairs

    Page(s): xviii
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  • Message from the Doctoral Symposium Co-chairs

    Page(s): xix
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  • Message from the Workshop Co-chairs

    Page(s): xx
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  • Organizing Committee

    Page(s): xxi - xxiii
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  • Program Committee

    Page(s): xxiv - xxvi
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  • Reviewers

    Page(s): xxvii - xxix
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  • Joint COMPSAC/SAINT Keynotes

    Page(s): xxx
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (71 KB)  

    Provides an abstract for each of the keynote presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings. View full abstract»

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  • Joint COMPSAC/SAINT Panel

    Page(s): xxxi
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  • HSNCE 2011 Message

    Page(s): xxxii
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  • SISS 2011 Message

    Page(s): xxxiii
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  • MidArch 2011 Message

    Page(s): xxxiv
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  • NETSAP 2011 Message

    Page(s): xxxv
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  • IC-Web 2011 Message

    Page(s): xxxvi - xxxvii
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  • EUCASS 2011 Message

    Page(s): xxxviii
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  • C3NET 2011 Message

    Page(s): xxxix
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  • FIE 2011 Message

    Page(s): xl - xli
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  • HEUNET 2011 Message

    Page(s): xlii - xliii
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  • ITeS 2011 Message

    Page(s): xliv
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  • Searching Structural Neighborhood of Malicious URLs to Improve Blacklisting

    Page(s): 1 - 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (392 KB) |  | HTML iconHTML  

    Filtering based on blacklists is a major countermeasure against malicious websites. However, blacklists must be updated because malicious URLs tend to be short-lived and their sub strings may be partially mutated to avoid blacklisting. Due to these characteristics, it can be assumed that unknown malicious URLs exist in the neighborhood of known malicious URLs, created by the same adversary. We propose an effective blacklist URL generation method. We try to discover the URLs in the neighborhood of a malicious URL by using a search engine. Those suspicious neighborhoods around malicious URLs require further investigation to determine their blacklisting candidacy. We experimentally evaluated the proposed generation method by using real blacklisted URLs for both drive-by-download and click-download infection. The results showed that the proposed method can effectively improve identification of malicious URLs and maintenance of the coverage of blacklists. View full abstract»

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