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Volumn 2836, Issue , 2003, Pages 325-336

A research on intrusion detection based on unsupervised clustering and support vector machine

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLUSTERING ALGORITHMS; INTRUSION DETECTION; MERCURY (METAL);

EID: 0142187790     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-39927-8_30     Document Type: Article
Times cited : (20)

References (17)
  • 4
    • 0034247240 scopus 로고    scopus 로고
    • Mining Fuzzy Association Rules and Fuzzy FrequencyEpisodes for Intrusion Detection
    • John Wiley & Sons
    • Luo J., Bridges S. M. Mining Fuzzy Association Rules and Fuzzy FrequencyEpisodes for Intrusion Detection. International Journal of Intelligent Systems John Wiley & Sons, 2000, 687-703
    • (2000) International Journal of Intelligent Systems , pp. 687-703
    • Luo, J.1    Bridges, S.M.2
  • 5
    • 0141797880 scopus 로고    scopus 로고
    • A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data
    • Kluwer
    • Eskin E., Arnold A., etc. A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data. Data Mining for Security Applications (DMSA-2002). Kluwer 2002
    • (2002) Data Mining for Security Applications (DMSA-2002)
    • Eskin, E.1    Arnold, A.2
  • 6
    • 1642375563 scopus 로고    scopus 로고
    • Adaptive Model Generation: An Architecture for the Deployment of Data Minig-based Intrusion Detection Systems
    • Kluwer
    • Honig A., Howard A,. etc. Adaptive Model Generation: An Architecture for the Deployment of Data Minig-based Intrusion Detection Systems. Data Mining for Security Applications (DMSA-2002). Kluwer 2002
    • (2002) Data Mining for Security Applications (DMSA-2002)
    • Honig, A.1    Howard, A.2
  • 8
    • 84943383590 scopus 로고    scopus 로고
    • Identifying Important Features for Intrusion Detection Using Support Vector Machines and Neural Networks
    • Proceedings. 2003 Symposium, 2003
    • Mukkamala S., Janowski G., etc. Identifying Important Features For Intrusion Detection Using Support Vector Machines and Neural Networks. Applications and the Internet, 2003. Proceedings. 2003 Symposium, 2003, 209-216
    • (2003) Applications and the Internet , pp. 209-216
    • Mukkamala, S.1    Janowski, G.2
  • 11
    • 11244287524 scopus 로고    scopus 로고
    • An Intrusion Detection Based on SVM
    • Rao X.. An Intrusion Detection Based on SVM. Journal of Software 2002, 14(4), 798-803
    • (2002) Journal of Software , vol.14 , Issue.4 , pp. 798-803
    • Rao, X.1
  • 15
    • 0000487102 scopus 로고    scopus 로고
    • Estimating the support of a high-dimensional distribution
    • Schölkopf. B, Platt. J. C., etc. Estimating the support of a high-dimensional distribution. Neural Computation. 2001, 13 (7), 1443-1471
    • (2001) Neural Computation , vol.13 , Issue.7 , pp. 1443-1471
    • Schölkopf, B.1    Platt, J.C.2
  • 16
    • 35248823921 scopus 로고    scopus 로고
    • http://www.csie.ntu.edu.tw/~cjlin/libsvm
  • 17
    • 35248883713 scopus 로고    scopus 로고
    • http://kdd.ics.uci.edu/databases/kddcup99/task.html


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.