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Volumn 17, Issue , 2004, Pages 88-98

Using active learning in intrusion detection

Author keywords

Active learning; Intrusion detection

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTER SYSTEMS; DATA ACQUISITION; SECURITY OF DATA;

EID: 4944242926     PISSN: 10636900     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (57)

References (25)
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    • E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo. A geometric framework for unsupervised anomaly detection. In D. Barbará and S. Jajodia, editors, Applications of Data Mining in Computer Security, Advances in Information Security, pages 77-101. Kluwer Academic Publishers, Boston, 2002.
    • (2002) Applications of Data Mining in Computer Security , pp. 77-101
    • Eskin, E.1    Arnold, A.2    Prerau, M.3    Portnoy, L.4    Stolfo, S.5
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  • 12
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    • T. Joachims. Making large-scale SVM learning practical. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning. The MIT Press, 1999.
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    • Toulouse, France, Oct. 2-4 Lecture Notes in Computer Science #1907, Springer-Verlag, Berlin
    • J. McHugh. The 1998 Lincoln Laboratory IDS evaluation: A critique. In Recent Advances in Intrusion Detection (RAID 2000), pages 145-161, Toulouse, France, Oct. 2-4 2000. Lecture Notes in Computer Science #1907, Springer-Verlag, Berlin.
    • (2000) Recent Advances in Intrusion Detection (RAID 2000) , pp. 145-161
    • McHugh, J.1
  • 19
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  • 20
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  • 24
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    • Valdes, A.1    Skinner, K.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.