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Experiments with the V-Detector algorithm

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Języki publikacji
EN
Abstrakty
EN
V-Detector is real-valued negative selection algorithm designed to detect anomalies in datasets containing real-valued data. Many of the previous experiments were focused on analysis of usability of this algorithm to detect intruders in computer network. Intrusion Detection System (IDS) should be efficient and reliable due to a large number of network connections and their diversity. Additionally, every connection is described as a record containing tens of numerical and symbolic attributes. We show that choosing appropriate representation of "typical" connections and smart decomposition of the learning data it is possible to obtain quite efficient and cheap algorithm detecting mom-typical connections.
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Czasopismo
Rocznik
Strony
55--63
Opis fizyczny
Bibliogr. 18 poz., wykr.
Twórcy
Bibliografia
  • [1] Balthrop J., Esponda F., Forrest S., Coverage and generalization in an Artificial Immune Systems, Proc. Genetic and Evolutionary Computation Conference, GECCO 2002, Morgan Kaufmann, New York 2002, pp. 3-10.
  • [2] Chmielewski A., Wierzchoń S.T., Badanie przydatności algorytmu generującego V-detectory do klasyfikacji wybranych zbiorów, VI Krajowa Konferencja Inżynieria Wiedzy i Systemy Ekspertowe, Wrocław 2006, pp. 13-22.
  • [3] Chmielewski A., Wierzchoń S.T., Comparing Real-Valued Negative Selection Algorithms for Intrusion Detection Applications, Proc. 13th Int. Multi-Conf. Advanced Computer Systems (ACS 2006), Międzyzdroje, Poland, Vol. I, October 18-20, 2006, pp. 387-395.
  • [4] Chmielewski A., Wierzchoń S.T., V-Detector algorithm with tree-based structures, Proc. Int. Multiconference on Computer Science and Information Technology, Wisła, Poland, November 6-10, 2006, pp. 9-14.
  • [5] Costa Branco P.J., Dente J.A., Vilela M, Using immunology principles for fault detection, IEEE Trans, on Industrial Electronics, 50, 2003, pp. 362-373.
  • [6] Dasgupta D., Forrest S., An anomaly detection algorithm inspired by the immune system, [in:] D. Dasgupta (ed.), Artificial Immune Systems and Their Applications, Springer-Verlag, 1999, pp. 262-277.
  • [7] Forrest S., Perelson A., Allen L., Cherukuri R., Self-nonself discrimination in a computer, Proc. IEEE Symp. Research in Security and Privacy, IEEE Computer Soc. Press, Los Alamitos, CA, 1994, pp. 202-212.
  • [8] González F., Dasgupta D., Nino L.F., A randomized real-valued negative selection algorithm, [in:] J. Timmis, P.J. Bentley, E. Hart (eds.), Proc. 2nd Int. Conf. Artificial Immune Systems (ICARIS-2003), LNCS 2787, Springer-Verlag, 2003, pp. 261-272.
  • [9] Hettich S., BAY S.D., KDD Cup 1999 Data (1999), http://kdd.ics.uci.edu.
  • [10] Ji Z., Dasgupta D., Real-valued negative selection algorithm with variable-sized detectors, [in:] Genetic and Evolutionary Computation GECCO-2004, Part I. LNCS 3102, Seattle, WA, USA, Springer-Verlag, 2004, pp. 287-298.
  • [11] Joachims T., Making large-scale SVM learning practical, Advances in: Kernel Methods - Support Vector Learning, B. Scholkopf and C. Burges and A. Smola (eds.), MIT-Press, 1999.
  • [12] Lehmann D., What is intrusion detection? [in:] SANS Institute - Intrusion Detection FAQ, http://www.sans.org/resources/idfaq/.
  • [13] Mé L., Cédric M., Intrusion detection: A bibliography, Tech. Rep. SSIR-2001-01, http://citeseer.ist.psu.edu/484682.html.
  • [14] Stibor T., On the appropriateness of negative selection for anomaly detection and network intrusion detection, Ph.D. Thesis, Fachbereich Informatik der Technischen Universitat Darmstadt, Darmstadt 2006.
  • [15] Stibor T., Timmis J., Eckert C., A comparative study of real-valued negative selection to statistical anomaly detection techniques, [in:] Proc. 4th Int. Conf. Artificial Immune Systems (ICARIS-2005), LNCS 3627, Springer-Verlag, 2005, pp. 262-275.
  • [16] Vapnik V.N. The Nature of Statistical Learning Theory. Springer, 1995.
  • [17] Wierzchoń S.T., Deriving concise description of non-self patterns in an artificial immune system, [in:] L.C. Jain, J. Kacprzyk (eds.), New Learning Paradigms in Soft Computing, Physica-Verlag, 2001, pp. 438-458.
  • [18] Wierzchoń S.T., Sztuczne systemy immunologiczne. Teoria i zastosowania, Akademicka Oficyna Wydawnicza ELIT, Warszawa 2001.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0027-0086
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