Abstract
The traditional network intrusion data detection brings a lot of problems, like congestion control, leading to poor clustering effect. An improved intrusion detection algorithm based on ant guide data clustering is proposed, the accuracy of the output probability is determined by the size of the gene-bit random number, updating status categories sufficient statistics to obtain invasion characteristics observation probability and initial probability, and performing cluster center update rules. Simulation results show that the algorithm has better application performance in data clustering and network intrusion detection.
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