Data clustering for the block profile method of intruder detection

• Autorzy: Wesolowski T. , Kudlacik P.
• Języki publikacji: EN

Abstrakty

EN

The paper concerns the problem of computer security and intrusion detection. In particular the problem of detecting the masqueraders – intruders who pretend to be authorized users of computer systems. The article presents an improvement of the block profile method of intruder detection for computer systems. As a base for the work the block profiles were taken and the clustering of the block profiles data was performed. The aim was to detect the masqueraders within the test data. The problem of computer security is particularly important in medical applications. Activities of intruder can distort the data stored in medical databases and analyzed by the medical expert systems or decision support systems and thus make it difficult to support the diagnosis of diseases. For this reason it is necessary protect the medical computer systems from unauthorized access. This article presents the preliminary research and conclusions.

Słowa kluczowe

EN

user-profiles; intruder detection; computer security; user verification

PL

profil użytkownika; wykrywanie intruza; bezpieczeństwo komputera; weryfikacja użytkownika

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2013
• Tom: Vol. 22
• Strony: 209--216
• Opis fizyczny: Bibliogr. 8 poz., wykr.

Twórcy

autor

Wesolowski T.

• University of Silesia, Institute of Computer Science, Bedzinska 39, 41-200 Sosnowiec, Poland

autor

Kudlacik P.

• University of Silesia, Institute of Computer Science, Bedzinska 39, 41-200 Sosnowiec, Poland

Bibliografia

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• [3] HASTIE T., TIBSHIRANI R., FRIEDMAN J., The Elements of Statistical Learning (2nd edition), Springer-Verlag, 2009.
• [4] PORWIK P., SOSNOWSKI M., WESOŁOWSKI T., WRÓBEL K., Computational Assessment of a Blood Vessels Compliance: A Procedure Based on Computed Tomography Coronary Angiography, LNAI, Springer, 2011, Vol. 6678/1, pp. 428-435.
• [5] SALEM M. B., HERSHKOP S., STOLFO S. J., A survey of insider attack detection research, Insider Attack and Cyber Security: Beyond the Hacker, Springer, 2008, pp. 69-90.
• [6] SCHONLAU M. et al., Computer intrusion: detecting masquerades, Statistical Science, 2001, Vol. 16, pp. 58-74.
• [7] SCHONLAU M., Masquerading user data, http://www.schonlau.net.
• [8] SCHONLAU M., THEUS M., Detecting Masquerades in Intrusion Detection Based on Unpopular Commands, Information Processing Letters 76, 2000, pp. 33-38.