Tan, L, Li, C, Xia, J and Cao, J (2019) Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection. COMPUTERS MATERIALS & CONTINUA, 61 (1). 275 - 288.

Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection.pdf - Published Version
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Due to the widespread use of the Internet, customer information is vulnerable to computer systems attack, which brings urgent need for the intrusion detection technology. Recently, network intrusion detection has been one of the most important technologies in network security detection. The accuracy of network intrusion detection has reached higher accuracy so far. However, these methods have very low efficiency in network intrusion detection, even the most popular SOM neural network method. In this paper, an efficient and fast network intrusion detection method was proposed. Firstly, the fundamental of the two different methods are introduced respectively. Then, the self-organizing feature map neural network based on K-means clustering (KSOM) algorithms was presented to improve the efficiency of network intrusion detection. Finally, the NSL-KDD is used as network intrusion data set to demonstrate that the KSOM method can significantly reduce the number of clustering iteration than SOM method without substantially affecting the clustering results and the accuracy is much higher than K-Means method. The Experimental results show that our method can relatively improve the accuracy of network intrusion and significantly reduce the number of clustering iteration.

Item Type: Article
Additional Information: All relevant information and final manuscripts can be found at; http://www.techscience.com/cmc/v61n1/23112
Uncontrolled Keywords: neural network, clustering, network intrusion detection.
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions: Faculty of Natural Sciences > School of Geography, Geology and the Environment
Related URLs:
Depositing User: Symplectic
Date Deposited: 24 Feb 2020 16:30
Last Modified: 04 Nov 2020 13:42
URI: https://eprints.keele.ac.uk/id/eprint/7683

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