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    Abstract
2013 (Vol. 5, Issue: 03)
Article Information:

New Intelligent Computer Intrusion Detection Method Using Hessian Local Linear Embedding and Multi-Kernel Support Vector Machine

Fei Hu, Guoxiang Zhong, Qiong Bo and Yang Lei
Corresponding Author:  Fei Hu 

Key words:  Computer networks, HLLE, intrusion detection, multi-kernel SVM, , ,
Vol. 5 , (03): 937-943
Submitted Accepted Published
June 20, 2012 July 23, 2012 January 21, 2013
Abstract:

Computer networks frequently collapse under the destructive intrusions. It is crucial to detection hidden intrusions to protect the computer networks. However, a computer intrusion often distributes high dimensional characteristic signals, which increases the difficulty of intrusion detection. Literature review indicates that limited work has been done to address the nonlinear dimension reduction problem in computer intrusion detection. Hence, this study has proposed a new intrusion detection method based on the Hessian Local Linear Embedding (HLLE) and multi-kernel Support Vector Machine (SVM). The HLLE was firstly used to reduce the dimension of the original intrusion date in a nonlinear manner. Then the SVM with multiply kernels was employed to detect the intrusions. A real computer network experimental system has been established to evaluate the proposed method. Four typical intrusions have been tested. The test results show high effectiveness of the new detection method. In addition, the new method has been compared with the single-kernel SVM with Local Linear Embedding (LLE) or Principal Component Analysis (PCA). The comparison results demonstrate that the proposed HLLE plus multi-kernel SVM can provide the best computer intrusion detection rate of 97.1%.
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  Cite this Reference:
Fei Hu, Guoxiang Zhong, Qiong Bo and Yang Lei, 2013. New Intelligent Computer Intrusion Detection Method Using Hessian Local Linear Embedding and Multi-Kernel Support Vector Machine.  Research Journal of Applied Sciences, Engineering and Technology, 5(03): 937-943.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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