A distributed neural network learning algorithm for network intrusion detection system

Authors:
Yanheng Liu;Daxin Tian;Xuegang Yu;Jian Wang
Affiliations:
College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China;College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China;College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China;College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

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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.