On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on Intrusion Detection Systems

作者：

Highlights：

• A methodology based on GFS and OVO in the framework of IDS is proposed.

• Linguistic labels enable smoother borderline, and allows higher interpretability.

• Divide-and-conquer learning scheme, improves precision for rare attack events.

• Several metrics of performance show the goodness of this approach on KDDCUP’99.

• Our results excels the state-of-the-art of GFS for IDS and C4.5 decision tree.

摘要

•A methodology based on GFS and OVO in the framework of IDS is proposed.•Linguistic labels enable smoother borderline, and allows higher interpretability.•Divide-and-conquer learning scheme, improves precision for rare attack events.•Several metrics of performance show the goodness of this approach on KDDCUP’99.•Our results excels the state-of-the-art of GFS for IDS and C4.5 decision tree.

论文关键词：Intrusion Detection Systems,Genetic Fuzzy Systems,Pairwise learning,One-vs-One,Misuse detection

论文评审过程：Available online 11 August 2014.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.08.002