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This paper aims to detect features of coordinated attacks by applying data mining techniques, namely Apriori with PrefixSpan, to the CCC DATAset 2008-2010, which comprises captured packet data and downloading logs. Data mining algorithms enable us to automate the detection of characteristics in large amounts of data, which conventional heuristics cannot deal with. Apriori achieves a high recall but with false positives, whereas PrefixSpan has high precision but low recall. We therefore propose a hybrid of these two algorithms. Our analysis shows a change in the behavior of malware over the past three years.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.21(2013) No.4 (online)DOI http://dx.doi.org/10.2197/ipsjjip.21.607------------------------------This paper aims to detect features of coordinated attacks by applying data mining techniques, namely Apriori with PrefixSpan, to the CCC DATAset 2008-2010, which comprises captured packet data and downloading logs. Data mining algorithms enable us to automate the detection of characteristics in large amounts of data, which conventional heuristics cannot deal with. Apriori achieves a high recall but with false positives, whereas PrefixSpan has high precision but low recall. We therefore propose a hybrid of these two algorithms. Our analysis shows a change in the behavior of malware over the past three years.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.21(2013) No.4 (online)DOI http://dx.doi.org/10.2197/ipsjjip.21.607------------------------------

収録刊行物

  • 情報処理学会論文誌

    情報処理学会論文誌 54(9), 2013-09-15

各種コード

  • NII論文ID(NAID)
    110009605625
  • NII書誌ID(NCID)
    AN00116647
  • 本文言語コード
    ENG
  • 資料種別
    Journal Article
  • ISSN
    1882-7764
  • データ提供元
    NII-ELS  IPSJ 
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