Abstract
This paper presents an anomaly detection approach for the network intrusion detection based on Cellular Neural Networks (CNN) model. CNN has features with multi-dimensional array of neurons and local interconnections among cells. Recurrent Perceptron Learning Algorithm (RPLA) is used to learn the templates and bias in CNN classifier. Experiments with KDD Cup 1999 network traffic connections which have been preprocessed with methods of features selection and normalization have shown that CNN model is effective for intrusion detection. In contrast to back propagation neural network, CNN model exhibits an excellent performance owing to the higher attack detection rate with lower false positive rate.
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© 2006 Springer-Verlag Berlin Heidelberg
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Yang, Z., Karahoca, A. (2006). An Anomaly Intrusion Detection Approach Using Cellular Neural Networks. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds) Computer and Information Sciences – ISCIS 2006. ISCIS 2006. Lecture Notes in Computer Science, vol 4263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11902140_94
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DOI: https://doi.org/10.1007/11902140_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47242-1
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