Abstract
Given the increasing number of users of computer systems and networks, it is difficult to know the profile of the latter and therefore the intrusion has become a highly prized of community of network security. In this paper to address the issues mentioned above, we used the data mining techniques namely association rules, decision trees and Bayesian networks. The results obtained on the KDD’99 benchmark has been validated by several evaluation measures, and are promising and provide access to other techniques and hybridization to improve the security and confidentiality in the field.
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Lokbani, A.C., Lehireche, A., Hamou, R.M. (2013). Experimentation of Data Mining Technique for System’s Security: A Comparative Study. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_30
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DOI: https://doi.org/10.1007/978-3-642-38715-9_30
Publisher Name: Springer, Berlin, Heidelberg
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