INTRUSION DETECTION METHOD BASED ON ICA ALGORITHM AND THREE-WAY DECISIONS
With the diversification and intelligence of network intrusion behaviors,network data has the characteristics of high feature dimensionality and non-linear separability,which leads to insufficient feature extraction and low model classification accuracy in network data.Therefore,an intrusion detection model based on independent component analysis(ICA)and three-way decisions(TWD)is proposed.The characteristics of network connection data were reduced by using ICA algorithm based on maximal non-Gauss property.The data was mapped from high dimensional feature space to low dimensional space to eliminate redundant data.And a multi-granular feature space was constructed through multiple feature extraction.Decisions were made on network behaviors based on three decision-making theories.Experiments were performed on NSL-KDD and CIC-IDS2017 data set.The results show that the proposed model has better feature extraction capability and more accurate classification ability.
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