A New Approach For Feature Selection In Intrusion Detection System

Rahim TAHERI, Marzieh AHMADZADEH, Mohammad Rafi KHARAZMI
1.979 482


Abstract. There are numerous intrusion detection systems with different methods for detection of the attacks whose main challenge is enhancement of the efficiency and accuracy rates. Therefore, development of methods to enhance their efficiency is necessary. In this paper, we are searching for a solution to reduce number of the features. Cuttlefish algorithm was used to investigate all of the features and numbers including 3, 5, 10, and 13 were used for the analysis. Artificial neural network was used as the evaluation function and the results of this paper were compared with other papers. Among the selected features, the number thirteen feature had the highest efficiency and could detect nearly all of the attacks.


Data mining, feature selection, intrusion detection, cuttlefish, multilayer neural network

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