Spark-Based Feature Selection Algorithm of Network Traffic Classification
Wenlong Ke, Yong Wang, Xiaochun Lei, Bizhong Wei · 2017
Currently, with the rapid increasing of data scale in network traffic classification, how to select traffic features efficiently is becoming a big challenge. In this paper, we propose a redundant window-based optimal feature subset discover algorithm for feature selection, which use the growth algorithm to discover the relevant features and use the shrink algorithm to eliminate the redundant ones. Window redundancy and a parallel computing framework called Spark is integrated into the algorithm, which improve the efficiency of the algorithm significantly. The experimental results show that our method has a good performance in terms of accuracy and scalability, and improves the execution efficiency of feature selection and traffic classification.