Intrusion Detection Based on GA and IFCM Clustering Algorithm
Wang Ya-na · Jisuanji gongcheng · 2013
Concerning that the Intuitionistic Fuzzy c-means(IFCM) clustering algorithm has a deficiency of easily falling into a local optimum,an improved IFCM which combines the traditional IFCM with the upgraded Genetic Algorithm(GA) is proposed.The traditional GA is upgraded from two aspects:the fitness standardization and the group diversification.The upgraded GA is more effective in global optimization,which can overcome the IFCM's shortcoming of local optimum.Then the improved IFCM algorithm is innovatively practised in intrasion detection,and contrastive experiments on data sets KDD CUP99 show that,compared with IFCM algorithm,this algorithm advances the clustering precision effectively and has good reliability and feasibility.