• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

IKPCA-ELM-based Intrusion Detection Method


An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.


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Cite this article

[IEEE Style]
H. Wang, C. Wang, Z. Shen, D. Lin, "IKPCA-ELM-based Intrusion Detection Method," KSII Transactions on Internet and Information Systems, vol. 14, no. 7, pp. 3076-3092, 2020. DOI: 10.3837/tiis.2020.07.019.

[ACM Style]
Hui Wang, Chengjie Wang, Zihao Shen, and Dengwei Lin. 2020. IKPCA-ELM-based Intrusion Detection Method. KSII Transactions on Internet and Information Systems, 14, 7, (2020), 3076-3092. DOI: 10.3837/tiis.2020.07.019.

[BibTeX Style]
@article{tiis:23730, title="IKPCA-ELM-based Intrusion Detection Method", author="Hui Wang and Chengjie Wang and Zihao Shen and Dengwei Lin and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2020.07.019}, volume={14}, number={7}, year="2020", month={July}, pages={3076-3092}}