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Volumn 245, Issue , 2009, Pages 15-38

Evade hard multiple classifier systems

Author keywords

Adversarial classification; Hardness of evasion; Multiple classifier systems; Spam filtering

Indexed keywords


EID: 70350241126     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-03999-7_2     Document Type: Conference Paper
Times cited : (20)

References (11)
  • 4
    • 0038330235 scopus 로고    scopus 로고
    • Fusion of multiple classifiers for intrusion detection in computer networks
    • Giacinto, G., Roli, F., Didaci, L.: Fusion of multiple classifiers for intrusion detection in computer networks. Pattern Recognition Letters 24(12), 1795-1803 (2003)
    • (2003) Pattern Recognition Letters , vol.24 , Issue.12 , pp. 1795-1803
    • Giacinto, G.1    Roli, F.2    Didaci, L.3
  • 5
    • 33749242256 scopus 로고    scopus 로고
    • Nightmare at test time: Robust learning by feature deletion
    • Cohen, W.W, Moore, A, eds, Pittsburgh, PA, pp, ACM, New York
    • Globerson, A., RowRis, S.T.: Nightmare at test time: robust learning by feature deletion. In: Cohen, W.W., Moore, A. (eds.) Proc. 23rd Int. Conf. Mach. Learn., Pittsburgh, PA, pp. 353-360. ACM, New York (2006)
    • (2006) Proc. 23rd Int. Conf. Mach. Learn , pp. 353-360
    • Globerson, A.1    RowRis, S.T.2
  • 6
    • 70350213013 scopus 로고    scopus 로고
    • Haindl, M., Kittler, J., Roli, F. (eds.): MCS 2007. LNCS, 4472. Springer, Heidelberg (2007)
    • Haindl, M., Kittler, J., Roli, F. (eds.): MCS 2007. LNCS, vol. 4472. Springer, Heidelberg (2007)
  • 7
    • 46249103547 scopus 로고    scopus 로고
    • Jorgensen, Z., Zhou, Y., Inge, M.: A multiple instance learning strategy for combating good word attacks on spam filters. J. Mach. Learn. Research 9, 1115-1146 (2008)
    • Jorgensen, Z., Zhou, Y., Inge, M.: A multiple instance learning strategy for combating good word attacks on spam filters. J. Mach. Learn. Research 9, 1115-1146 (2008)
  • 10
    • 60349101742 scopus 로고    scopus 로고
    • Using an ensemble of one-class svm classifiers to harden payload-based anomaly detection systems
    • Hong Kong, pp, IEEE Comp. Soc, Los Alamitos
    • Perdisci, R., Gu, G., Lee, W.: Using an ensemble of one-class svm classifiers to harden payload-based anomaly detection systems. In: Proc. IEEE Int. Conf. Data Mining, Hong Kong, pp. 488-498. IEEE Comp. Soc., Los Alamitos (2006)
    • (2006) Proc. IEEE Int. Conf. Data Mining , pp. 488-498
    • Perdisci, R.1    Gu, G.2    Lee, W.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.