Abstract
Now a day’s, Intrusion detection is a very important research area in network security. Machine learning techniques have been applied to the field of intrusion detection. In this paper, we use KDD Cup 99’ data set for taking samples. For these samples we use classification algorithms to classify the network traffic data. In this paper, we are going to compare our results with features selected using Naive Bayes, Neural Networks. We are trying to use standard measurements like detection rate, false positive, false negative, accuracy and Confusion Matrix.
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Sravani, K., Srinivasu, P. (2014). Comparative Study of Machine Learning Algorithm for Intrusion Detection System. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Advances in Intelligent Systems and Computing, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-02931-3_23
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DOI: https://doi.org/10.1007/978-3-319-02931-3_23
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02930-6
Online ISBN: 978-3-319-02931-3
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