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.

Keywords:

Feature relevance, intrusion detection, intrusion feature selection, optimization searching, particle swarm optimization,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.