Abstract
To make network intrusion detection systems can be used in Gigabit Ethernet, a distributed neural network learning algorithm (DNNL) is put forward to keep up with the increasing network throughput. The main idea of DNNL is splitting the overall traffic into subsets and several sensors learn them in parallel way. The advantage of this method is that the large data set can be split randomly thus reduce the complicacy of the splitting algorithm. The experiments are performed on the KDD’99 Data Set which is a standard intrusion detection benchmark. Comparisons with other approaches on the same benchmark show that DNNL can perform detection with high detection rate.
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Liu, Y., Tian, D., Yu, X., Wang, J. (2006). A Distributed Neural Network Learning Algorithm for Network Intrusion Detection System. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_23
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DOI: https://doi.org/10.1007/11893295_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
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