Network intrusion detection algorithm based on quantum-behaved particle swarm optimization
LI Zheng-ji · Computer Engineering and Applications Journal · 2011
A hybrid algorithm based on quantum-behaved particle swarm optimization algorithm and semi-supervised fuzzy kernel clustering algorithm is proposed.It overcomes the drawbacks of fuzzy clustering methods which are sensitive to the initial cluster centers and easily trapped into local minima.The few labeled data can generate correct model with supervised clustering,and then the model aids to guide lots of unlabeled data to clustering,enlarges the numbles of labeled data.Those data that still can’t be labeled are clustered by the fuzzy kernel method based on quantum-behaved particle swarm optimization,and determine marker types.KDD CUP99 data set is implemented to evaluate the proposed algorithm.Compared to other algorithms,the results show the outstanding performance of the proposed algorithm.