NETWORK INTRUSION FEATURE SELECTION BASED ON MUTUAL INFORMATION AND FIREFLY ALGORITHM
In order to reduce redundant features in network intrusion detection data,this paper proposes a feature selection method based on mutual information and firefly algorithm.Aimed at the imprecision calculation of redundancy between features for mutual information,a feature selection method for inner class feature redundancy mutual information was proposed.In order to solve the problem that the fixed step factor in firefly algorithm made the algorithm fall into local optimum,the feature selection of adaptive step size firefly algorithm was proposed.After the feature subset was selected by the above methods,the optimal subset was selected by using the voting strategy.The intrusion detection based on C4.5 and Bayesian network classifier was carried out for this subset.The experimental results show that using 10 features can effectively improve the intrusion detection rate,false alarm rate and F-Measure,and also shorten the training and detection time.In addition,compared with the existing methods,this method achieves good results in accuracy,detection rate and F-Measure.
国家科技期刊平台
NSTL
万方数据
维普
