Intrusion detection based on immune principles and fuzzy association rules

Abstract

In this paper, a new intrusion detection method based on immune principles and fuzzy association rules is proposed. The proposed method uses fuzzy association rules for building fuzzy classifiers, which is also the detection engine of the intrusion detection system. A novel immune-inspired algorithm is proposed for mining fuzzy association rule set, in which the fuzzy sets corresponding to each attribute and the final fuzzy rule set can be directly extracted from a given data set. The KDD-99 dataset is used to evaluate the performance of the proposed algorithm and compared with other relevant intrusion detection methods. The results show the detection performances of the proposed algorithm are comparable with other relevant intrusion detection systems. © 2013 Springer-Verlag.