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Volumn 3617 LNCS, Issue , 2005, Pages 66-74

Combining genetic-based misuse and anomaly detection for reliably detecting intrusions in computer networks

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; CLASSIFICATION (OF INFORMATION); DATABASE SYSTEMS; GENETIC ALGORITHMS; PACKET NETWORKS; PATTERN RECOGNITION; SECURITY OF DATA; TELECOMMUNICATION TRAFFIC;

EID: 33745157910     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11553595_8     Document Type: Conference Paper
Times cited : (3)

References (16)
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  • 2
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    • Axelsson, S.1
  • 4
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    • A study in using neural networks for anomaly and misuse detection
    • Aug. 26-29 Washington DC
    • A.K. Ghosh, A. Schwartzbard, "A Study in Using Neural Networks for Anomaly and Misuse Detection", Proc. 8'th USENIX Security Symposium, Aug. 26-29 1999, Washington DC.
    • (1999) Proc. 8'th USENIX Security Symposium
    • Ghosh, A.K.1    Schwartzbard, A.2
  • 5
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    • Temporal sequence learning and data reduction for anomaly detection
    • T. Lane, C.E. Bradley, "Temporal Sequence learning and data reduction for anomaly detection", ACM Trans, on Inform, and System Security, vol. 2, no. 3, pp. 295-261, 1999.
    • (1999) ACM Trans, on Inform, and System Security , vol.2 , Issue.3 , pp. 295-1261
    • Lane, T.1    Bradley, C.E.2
  • 6
    • 84885774862 scopus 로고    scopus 로고
    • A framework for constructing features and models for intrusion detection systems
    • W. Lee, S.J. Stolfo, "A framework for constructing features and models for intrusion detection systems", ACM Transactions on Inform. System Security, vol. 3, no. 4, pp. 227-261, 2000.
    • (2000) ACM Transactions on Inform. System Security , vol.3 , Issue.4 , pp. 227-261
    • Lee, W.1    Stolfo, S.J.2
  • 8
    • 0035402096 scopus 로고    scopus 로고
    • Training a neural network based intrusion detector to recognize novel attack
    • S. C. Lee, D.V. Heinbuch, "Training a neural Network based intrusion detector to recognize novel attack", IEEE Trans. Syst, Man., and Cybernetic, Part-A, vol. 31, pp. 294-299, 2001.
    • (2001) IEEE Trans. Syst, Man., and Cybernetic, Part-A , vol.31 , pp. 294-299
    • Lee, S.C.1    Heinbuch, D.V.2
  • 10
    • 0038330235 scopus 로고    scopus 로고
    • Fusion of multiple classifiers for intrusion detection in computer networks
    • G. Giacinto, F. Roli, L. Didaci, "Fusion of multiple classifiers for intrusion detection in computer networks", Pattern Recognition Letters, vol. 24, pp. 1795-1803, 2003.
    • (2003) Pattern Recognition Letters , vol.24 , pp. 1795-1803
    • Giacinto, G.1    Roli, F.2    Didaci, L.3
  • 11
    • 27244444099 scopus 로고    scopus 로고
    • A modular multiple classifier system for the detection of intrusions
    • G. Giacinto, F. Roli, L. Didaci, "A Modular Multiple Classifier System for the Detection of Intrusions", Lecture Notes in Computer Science vol. 2709, pp. 346-355, 2003.
    • (2003) Lecture Notes in Computer Science , vol.2709 , pp. 346-355
    • Giacinto, G.1    Roli, F.2    Didaci, L.3
  • 12
    • 27244448490 scopus 로고    scopus 로고
    • Network intrusion detection by a multi stage classification system
    • Springer, Berlin
    • L. P. Cordella, A. Limongiello, C. Sansone, "Network Intrusion Detection by a Multi Stage Classification System", Lecture Notes in Computer Science vol. 3077, Springer, Berlin, pp. 324-333, 2004.
    • (2004) Lecture Notes in Computer Science , vol.3077 , pp. 324-333
    • Cordella, L.P.1    Limongiello, A.2    Sansone, C.3
  • 13
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    • The base-rate fallacy and the difficulty of intrusion detection
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  • 15
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    • Testing intrusion detection systems: A critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln laboratory
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    • McHugh, J.1
  • 16
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    • A genetic clustering method for intrusion detection
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    • Liu, Y.1    Chen, K.2    Liao, X.3    Zhang, W.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.