Non-negative Matrix Factorization Optimization and Its Application in Network Intrusion Detection

doi:10.3969/j.issn.1671-1122.2018.08.010

Abstract

Abstract:

Due to the non-negative matrix factorization (NMF) can effectively reduce the high-dimensional data to the low-dimension by decomposition, the initialization problem of non-negative matrix factorization algorithm was optimized by combining principal component analysis algorithm, and then it was applied to intrusion detection. For the problem of how to determine the number of reserved bases K and NMF matrix initialization, the application of the improved NMF algorithm in the field of network intrusion is studied. In order to achieve qualitative analysis, we reduce KDD dataset records from high-dimensional space to low-dimensional space, and then display data features in low-dimensional space. To achieve quantitative analysis, the data is classified by SVM and processed into test reports to verify that the optimized NMF algorithm is better than the original algorithm in detection rate and efficiency.

Key words: network security, intrusion detection, non-negative matrix factorization, SVM

CLC Number:

TP309

Gelin ZHANG, Yong LI. Non-negative Matrix Factorization Optimization and Its Application in Network Intrusion Detection[J]. Netinfo Security, 2018, 18(8): 73-78.

Figures/Tables 6

References 17

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