基于粒子群优化的k均值算法在网络入侵检测中的应用

Research of K-means algorithm based on particle swarm optimization in network intrusion detection

在k均值算法基础上,提出一种将粒子群算法与k均值算法相结合产生基于粒子群的k均值算法(PSO-k均值算法)。用KDD cup99数据集进行评估k均值算法和PSO-k算法检测性能。试验结果表明,PSO-k均值算法能够避免k均值算法固有的缺点,检测率提高和误报率下降,并且有较高的检测性能。

Based on the k-means algorithm, we proposed the PSO-k-means algorithm combining the k-means with PSO. Then KDD cup 99 dataset was applied to both algorithms. The experiment indicates that the PSO-k-means algorithm can avoid the inherent shortcomings of the k-means algorithm, and has higher detection performance with detection rate rising and false alarm rate falling.