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Volumn 96 AISC, Issue , 2011, Pages 293-303

Hybrid intelligent intrusion detection scheme

Author keywords

Deep belief network (DBN); Dimensional reduction; Network intrusion detection system; Support vector machines (SVMs)

Indexed keywords

SOFT COMPUTING; STATISTICAL TESTS; SUPPORT VECTOR MACHINES;

EID: 84867781089     PISSN: 18675662     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-20505-7_26     Document Type: Conference Paper
Times cited : (182)

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  • 2
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    • Intrusion detection techniques And approaches
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    • Verwoerd, T.1    Hunt, R.2
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    • State transition analysis: A rule-based intrusion detection approach
    • Ilgun, K., Kemmerer, R.A., Porras, P.A.: State transition analysis: A rule-based intrusion detection approach. IEEE Trans. Software Eng. 21, 181-199 (1995)
    • (1995) IEEE Trans. Software Eng. , vol.21 , pp. 181-199
    • Ilgun, K.1    Kemmerer, R.A.2    Porras, P.A.3
  • 10
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    • Anomaly-based intrusion detection: Privacy concerns and other problems
    • Lundin, E., Jonsson, E.: Anomaly-based intrusion detection: privacy concerns and other problems. Computer Networks 34, 623-640 (2002)
    • (2002) Computer Networks , vol.34 , pp. 623-640
    • Lundin, E.1    Jonsson, E.2
  • 11
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    • The use of computational intelligence in intrusion detection systems: A review
    • Wu, S., Banzhaf, W.: The use of computational intelligence in intrusion detection systems: A review. Applied Soft Computing 10, 1-35 (2010)
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    • Wu, S.1    Banzhaf, W.2
  • 13
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    • A fast learning algorithm for deep belief nets
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    • Hinton, G.E.1
  • 19
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    • CA, USA July
    • KDD 1999 dataset Irvine, CA, USA (July 2010), http://kdd.ics.uci.edu/ databases
    • (2010) KDD 1999 Dataset Irvine


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.