Network Security Behavior Recognition Based on Consensus Decision-Making Feature Selection
Yang Yu, Li Mao, Xiao Feng Wang · Applied Mechanics and Materials · 2014
Due to the large amount of network data and complex representation, traditional network security behavior recognition system always leads to high redundancy and dimension, resulting in taking up more resources, larger computation. To solve this problem, we do the features selection. This article presents a consensus decision-making method, which combines current famous feature selection algorithms to obtain a more reasonable result and to sort the features in order of importance to facilitate the appropriate selection of features under different conditions. With this method tested on SVM (Support Vector Machine) as classification algorithm, it proves that the algorithm effectively improves the recognition accuracy with fewer features and performs better in terms of result stability.