Abstract
A serial multi-stage classification system for facing the problem of intrusion detection in computer networks is proposed. The whole decision process is organized into successive stages, each one using a set of features tailored for recognizing a specific attack category. All the stages employ suitable criteria for estimating the reliability of the performed classification, so that, in case of uncertainty, information related to a possible attack are only logged for further processing, without raising an alert for the system manager. This permits to reduce the number of false alarms.
The proposed multi-stage intrusion detection system has been tested on two different services (http and ftp) of a standard database used for benchmarking intrusion detection systems. The experimental analysis highlights the effectiveness of the approach: the proposed system behaves significantly better than other multi-expert systems performing classification in a single stage.
This work has been partially supported by the Ministero dell’Istruzione, dell’Università e della Ricerca (MIUR) in the framework of the FIRB Project ”Middleware for advanced services over large-scale, wired-wireless distributed systems (WEB-MINDS)”.
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Cordella, L.P., Limongiello, A., Sansone, C. (2004). Network Intrusion Detection by a Multi-stage Classification System. In: Roli, F., Kittler, J., Windeatt, T. (eds) Multiple Classifier Systems. MCS 2004. Lecture Notes in Computer Science, vol 3077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25966-4_32
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DOI: https://doi.org/10.1007/978-3-540-25966-4_32
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