Experiments with the V-Detector algorithm

• Autorzy: Chmielewski A. , Wierzchoń S. T.
• 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.

Słowa kluczowe

EN

V-Detector; experiments

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2006
• Tom: Vol. 32, no 4
• Strony: 55--63
• Opis fizyczny: Bibliogr. 18 poz., wykr.

Twórcy

autor

Chmielewski A.

autor

Wierzchoń S. T.

• Faculty of Computer Science, Technical University of Białystok, Poland,

Bibliografia

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• [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.
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• [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.
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• [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.