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 Perception 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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Yang, Z., & Karahoca, A. (2006). An anomaly intrusion detection approach using cellular neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4263 LNCS, pp. 908–917). Springer Verlag. https://doi.org/10.1007/11902140_94
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