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Volumn 4430 LNCS, Issue , 2007, Pages 141-151

TCM-KNN algorithm for supervised network intrusion detection

Author keywords

Data mining; Machine learning; Network security; Supervised intrusion detection; TCM; TCM KNN algorithm

Indexed keywords

DATA MINING; DATA STRUCTURES; INTRUSION DETECTION; LEARNING ALGORITHMS; LEARNING SYSTEMS;

EID: 38049171048     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-71549-8_12     Document Type: Conference Paper
Times cited : (6)

References (21)
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    • Ye, N.: A Markov Chain Model of Temporal Behavior for Anomaly Detection. Proceedings of the 2000 IEEE Systems, Man, and Cybernetics Information Assurance and Security Workshop, (2000)
  • 8
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    • A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data
    • D. Barbara and S. Jajodia editors, Kluwer
    • Eskin, E., Arnold, A., Prerau, M., Portnoy, L., Stolfo, S. J.: A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data. In D. Barbara and S. Jajodia (editors), Applications of Data Mining in Computer Security, Kluwer (2002)
    • (2002) Applications of Data Mining in Computer Security
    • Eskin, E.1    Arnold, A.2    Prerau, M.3    Portnoy, L.4    Stolfo, S.J.5
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    • Prediction algorithms and confidence measure based on algorithmic randomness theory
    • Gammerman, A., Vovk, V.: Prediction algorithms and confidence measure based on algorithmic randomness theory. Theoretical Computer Science. (2002) 209-217
    • (2002) Theoretical Computer Science , pp. 209-217
    • Gammerman, A.1    Vovk, V.2
  • 14
    • 84885774862 scopus 로고    scopus 로고
    • Stolfo: A Framework for Constructing Features and Models for Intrusion Detection Systems
    • Wenke Lee, Salvatore J. Stolfo: A Framework for Constructing Features and Models for Intrusion Detection Systems. ACM Transactions on Information and System Security (TISSEC), Volume 3, Issue 4 (2000)
    • (2000) ACM Transactions on Information and System Security (TISSEC) , vol.3 , Issue.4
    • Lee, W.1    Salvatore, J.2
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    • Sara Matzner Chris Sinclair, Lyn Pierce: An Application of Machine Learning to Network Intrusion Detection, Proceedings of the 15th Annual Computer Security Applications Conference, Phoenix, AZ, USA (1999) 371-377
    • Sara Matzner Chris Sinclair, Lyn Pierce: An Application of Machine Learning to Network Intrusion Detection, Proceedings of the 15th Annual Computer Security Applications Conference, Phoenix, AZ, USA (1999) 371-377
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    • Bridges: Mining Fuzzy Association Rules and Fuzzy Frequency Episodes for Intrusion Detection
    • Jianxiong Luo, Susan M. Bridges: Mining Fuzzy Association Rules and Fuzzy Frequency Episodes for Intrusion Detection. International Journal of Intelligent Systems, Vol. 15, No. 8, (2000) 687-704
    • (2000) International Journal of Intelligent Systems , vol.15 , Issue.8 , pp. 687-704
    • Luo, J.1    Susan, M.2
  • 19
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    • Robert K. Cunningham: Improving Intrusion Detection Performance Using Keyword Selection and Neural Networks
    • Richard P. Lippmann, Robert K. Cunningham: Improving Intrusion Detection Performance Using Keyword Selection and Neural Networks. Computer Networks (2000) 597-603
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    • WEKA software, The University of Waikato, Hamilton, New Zealand
    • WEKA software, Machine Learning, http://www.cs.waikato.ac.nz/ml/weka/, The University of Waikato, Hamilton, New Zealand.
    • Machine Learning


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.