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2013 (Vol. 5, Issue: 07)
Article Information:

The Research on Intrusion Feature Selection Algorithm Based on Particle Swarm Optimization

Wang Yuanzhi and Ge Wengeng
Corresponding Author:  Wang Yuanzhi 

Key words:  Feature relevance, intrusion detection, intrusion feature selection, optimization searching, particle swarm optimization, ,
Vol. 5 , (07): 2360-2364
Submitted Accepted Published
July 12, 2012 August 15, 2012 March 11, 2013
Abstract:

High-dimensional intrusion detection data concentration information redundancy results in low processing velocity of intrusion detection algorithm. Accordingly, the current study proposes an intrusion feature selection algorithm based on Particle Swarm Optimization (PSO). Analyzing the features of the relevance between network intrusion data allows the PSO algorithm to optimally search in a featured space and autonomously select effective feature subset to reduce data dimensionality. Experimental results reveal that algorithm can effectively eliminate redundancy and reduce intrusion feature selection time to effectively increase the detection velocity of the system while ensuring detection accuracy rate.
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  Cite this Reference:
Wang Yuanzhi and Ge Wengeng, 2013. The Research on Intrusion Feature Selection Algorithm Based on Particle Swarm Optimization.  Research Journal of Applied Sciences, Engineering and Technology, 5(07): 2360-2364.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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