Nearest neighbor (NN) models play an important role in the intrusion detection system (IDS). However, with the advent of the era\nof big data, the NN model has the disadvantages of low efficiency, noise sensitivity, and high storage requirement. This paper\npresents a neighbor prototype selection method based on CCHPSO for intrusion detection. In the model, the prototype selection\nand feature weight adjustment are performed simultaneously and k-nearest neighbor (KNN) is used as the basic classifier. To deal\nwith large-scale optimization problems, a cooperative coevolving algorithm based on hybrid standard particle swarm and binary\nparticle swarm optimization, which employs the divide-and-conquer strategy, is proposed in this paper. Meanwhile, a fitness\nfunction based on the accuracy and data reduction rate is defined in the CCHPSO to obtain a set of appropriate prototypes and\nfeature weights. The KDD99 and NSL datasets are used to assess the effectiveness of the method. The empirical results indicate that\nthe data reduction rate of the proposed method is very high, ranging from 82.32% to 92.01%. Compared with all the data used, the\nproposed method can not only achieve comparable accuracy performance but also save a lot of storage and computing resources.
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