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Volumn 5, Issue 6, 2009, Pages 1825-1831

Machine learning-based intrusion detection algorithms

Author keywords

Artificial Neural Networks; Intrusion Detection; KDD CUP'99; Support Vector Machines

Indexed keywords

ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORKS; DATA SETS; INTRUSION DETECTION ALGORITHMS; INTRUSION DETECTION APPROACHES; MACHINE-LEARNING; NETWORK ATTACK; NETWORK INTRUSION DETECTION; NEW APPROACHES; ON-MACHINES; RESEARCH AREAS;

EID: 77954276704     PISSN: 15539105     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (46)

References (17)
  • 1
    • 34250801472 scopus 로고    scopus 로고
    • A hybrid machine learning approach to network anomaly detection
    • T. Shon and J. Moon. A hybrid machine learning approach to network anomaly detection. Information Sciences, vol.177, pp.3799-3821, 2007
    • (2007) Information Sciences , pp. 3799-3821
    • Shon, T.1    Moon, J.2
  • 2
    • 0003902428 scopus 로고    scopus 로고
    • Cryptography and network security principles and practices
    • USA: Prentice Hall
    • W. Stallings. Cryptography and network security principles and practices. USA: Prentice Hall, 2006
    • (2006)
    • Stallings, W.1
  • 4
    • 69249230890 scopus 로고    scopus 로고
    • Intrusion detection by machine learning: A review
    • C.F. Tsai, Y.F Hsu and C.Y. Lin etc. Intrusion detection by machine learning: A review. Expert Systems with Applications, 36(10), pp.11994-12000, 2009
    • (2009) Expert Systems with Applications , vol.36 , Issue.10 , pp. 11994-12000
    • Tsai, C.F.1    Hsu, Y.F.2    Lin, C.Y.3
  • 5
    • 84885774862 scopus 로고    scopus 로고
    • A framework for constructing features and models for intrusion detection systems
    • W. Lee and S.J. Stolfo. A framework for constructing features and models for intrusion detection systems. ACM Transaction on Information and System Security. 3(4), pp.227-261,2007
    • (2007) ACM Transaction on Information and System Security , vol.3 , Issue.4 , pp. 227-261
    • Lee, W.1    Stolfo, S.J.2
  • 8
    • 0036321445 scopus 로고    scopus 로고
    • Use of k-nearest neighbor classifier for intrusion detection
    • Y. Liao and V. R. Vemuri. Use of k-nearest neighbor classifier for intrusion detection. Computers and Security, 21(5), pp.439-448, 2002
    • (2002) Computers and Security , vol.21 , Issue.5 , pp. 439-448
    • Liao, Y.1    Vemuri, V.R.2
  • 9
    • 0141797880 scopus 로고    scopus 로고
    • A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data
    • E. Eskin, A. Arnold and M. Prerau, etc. A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data.Applicatons of Data Mining in Computer Security, 2002
    • (2002) Applicatons of Data Mining in Computer Security
    • Eskin, E.1    Arnold, A.2    Prerau, M.3
  • 11
    • 0009900351 scopus 로고    scopus 로고
    • Anomaly detection over noisy data using learned probability distributions
    • San Francisco, CA
    • E. Eskin. Anomaly detection over noisy data using learned probability distributions. In Proc of: the 17th International Conference on Machine Learning, San Francisco, CA, pp.255-262, 2000
    • (2000) Proc of: the 17th International Conference on Machine Learning , pp. 255-262
    • Eskin, E.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.