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Volumn 40, Issue , 2013, Pages 1-6

Anomaly intrusion detection based on PLS feature extraction and core vector machine

Author keywords

Anomaly intrusion detection; Core vector machine; Feature extraction; Partial least square; Support Vector Machine

Indexed keywords

ANOMALY INTRUSION; ANOMALY INTRUSION DETECTION; COMBINED ALGORITHMS; DATA SETS; FEATURE SETS; PARTIAL LEAST SQUARE (PLS); SAMPLE DATA; VECTOR MACHINES;

EID: 84872967561     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2012.09.004     Document Type: Article
Times cited : (77)

References (19)
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    • H. Peng Research of intrusion detection method based on rough set Journal of UEST of China 35 1 2006 108 110
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    • Peng, H.1
  • 10
    • 0141797880 scopus 로고    scopus 로고
    • A geometric framework for unsupervised anomaly detection: Detecting intrusion in unlabeled data
    • Kluwer
    • E. Eskin, A. Amokd, and M. Prerau A geometric framework for unsupervised anomaly detection: detecting intrusion in unlabeled data Applications of Data Mining in Computer Security 2002 Kluwer 537 547
    • (2002) Applications of Data Mining in Computer Security , pp. 537-547
    • Eskin, E.1    Amokd, A.2    Prerau, M.3
  • 12
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  • 15
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    • Core vector machines: Fast SVM training on very large data sets
    • Software
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    • Tsang, I.W.1    Kwok, J.T.2    Cheung, P.M.3
  • 16
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    • Partial least squares for discrimination
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.