An intrusion detection approach based on multiple rough classifiers integration
Lin Feng · Journal of Experimental & Theoretical Artificial Intelligence · 2011
The study of intrusion detection techniques has been one of the hot spot topics in the field of network security in recent years. For high-dimensional intrusion detection data sets and a single classifier's weak classification ability for data sets with many classes, a novel intrusion detection approach, termed intrusion detection based on multiple rough classifiers integration, is proposed. First, some training data sets are generated from intrusion detection data by random sampling. By combing rough sets and quantum genetic algorithm, a subset of attributes is selected. Then, each simplified data set is trained, which establishes a group of rough classifiers. Finally, the intrusion data classification result is obtained according to the absolute majority voting strategy. The experimental results illustrate the effectiveness of our methods.