Abstract
Many current IDSs are constructed by manual encoding of expert knowledge; changes to IDSs are expensive and slow. In this paper, we describe adaptively building Intrusion Detection (ID) models. The Central idea is to utilize auditing programs to extract an extensive set of features that describe each network connection or host session, and apply data mining programs to learn rules that accurately capture the behavior of intrusions and normal activities. We used an efficient algorithm for rule generation IREP++, which is able to produce rule sets more quickly and often express the target concept with fewer rules and fewer literals per rule resulting in a concept description that is easier for humans to understand. A new data structure (T-tree) for Association Rule Mining (ARM) is described.
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© 2006 Springer-Verlag Berlin Heidelberg
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Raju S., R., Rao, S. (2006). Construction of Adaptive IDS Through IREP++ and ARM. In: Chaudhuri, S., Das, S.R., Paul, H.S., Tirthapura, S. (eds) Distributed Computing and Networking. ICDCN 2006. Lecture Notes in Computer Science, vol 4308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11947950_14
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DOI: https://doi.org/10.1007/11947950_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68139-7
Online ISBN: 978-3-540-68140-3
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