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Volumn 5781 LNAI, Issue PART 1, 2009, Pages 407-422

Active and semi-supervised data domain description

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; INTRUSION DETECTION; LEARNING ALGORITHMS; OBJECT DETECTION; OBJECT RECOGNITION; OPTIMIZATION; SUPERVISED LEARNING;

EID: 70350627210     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04180-8_44     Document Type: Conference Paper
Times cited : (33)

References (33)
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  • 12
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    • Minimum enclosing and maximum excluding machine for pattern description and discrimination
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    • (2006) ICPR 2006: Proceedings of the 18th International Conference on Pattern Recognition , pp. 129-132
    • Liu, Y.1    Zheng, Y.F.2
  • 13
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    • Distinctive image features from scale invariant keypoints
    • Lowe, D.: Distinctive image features from scale invariant keypoints. International Journal of Computer Vision 60(2), 91-110 (2004)
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    • Lowe, D.1
  • 18
    • 0003120218 scopus 로고    scopus 로고
    • Fast training of support vector machines using sequential minimal optimization
    • Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Advances in kernel methods: support vector learning (1999)
    • (1999) Advances in kernel methods: Support vector learning
    • Platt, J.C.1
  • 19
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    • Detecting unknown network attacks using language models
    • Büschkes, R, Laskov, P, eds, DIMVA 2006, Springer, Heidelberg
    • Rieck, K., Laskov, P.: Detecting unknown network attacks using language models. In: Büschkes, R., Laskov, P. (eds.) DIMVA 2006. LNCS, vol. 4064, pp. 74-90. Springer, Heidelberg (2006)
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    • Rieck, K.1    Laskov, P.2
  • 20
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    • Language models for detection of unknown attacks in network traffic
    • Rieck, K., Laskov, P.: Language models for detection of unknown attacks in network traffic. Journal in Computer Virology 2(4), 243-256 (2007)
    • (2007) Journal in Computer Virology , vol.2 , Issue.4 , pp. 243-256
    • Rieck, K.1    Laskov, P.2
  • 22
    • 70350630379 scopus 로고    scopus 로고
    • Aladin: Active learning of anomalies to detect intrusion
    • Technical report, Microsoft Research
    • Stokes, J.W., Platt, J.C.: Aladin: Active learning of anomalies to detect intrusion. Technical report, Microsoft Research (2008)
    • (2008)
    • Stokes, J.W.1    Platt, J.C.2
  • 23
  • 24
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    • Support vector data description
    • Tax, D.M.J., Duin, R.P.W.: Support vector data description. Machine Learning 54, 45-66 (2004)
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  • 27
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    • Wang, K., Parekh, J.J., Stolfo, S.J.: Anagram: A content anomaly detector resistant to mimicry attack. In: Zamboni, D., Krügel, C. (eds.) RAID 2006. LNCS, 4219, pp. 226-248. Springer, Heidelberg (2006)
    • Wang, K., Parekh, J.J., Stolfo, S.J.: Anagram: A content anomaly detector resistant to mimicry attack. In: Zamboni, D., Krügel, C. (eds.) RAID 2006. LNCS, vol. 4219, pp. 226-248. Springer, Heidelberg (2006)
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    • Wang, K., Stolfo, S.J.: Anomalous payload-based network intrusion detection. In: Jonsson, E., Valdes, A., Almgren, M. (eds.) RAID 2004. LNCS, 3224, pp. 203-222. Springer, Heidelberg (2004)
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    • Semi-supervised learning in literature survey
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.