Modification of supervised OPF-based intrusion detection systems using unsupervised learning and social network concept

作者：

Highlights：

• We proposed a modified version of optimum-path forest (MOPF) for intrusion detection.

• Social network analysis is used for pruning the training set to speed up the OPF.

• A partitioning module is used to improve the detection rate of low-frequent attacks.

• The classification phase of traditional OPF is modified for improving the accuracy.

• Our method improved detection/false alarm rate and execution time of traditional OPF.

摘要

•We proposed a modified version of optimum-path forest (MOPF) for intrusion detection.•Social network analysis is used for pruning the training set to speed up the OPF.•A partitioning module is used to improve the detection rate of low-frequent attacks.•The classification phase of traditional OPF is modified for improving the accuracy.•Our method improved detection/false alarm rate and execution time of traditional OPF.

论文关键词：Optimum-path forest,Classification,Clustering,Pruning,Centrality,Prestige,Social network analysis

论文评审过程：Received 23 June 2015, Revised 11 July 2016, Accepted 22 August 2016, Available online 27 August 2016, Version of Record 7 September 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.08.027