Modification of supervised OPF-based intrusion detection systems using unsupervised learning and social network concept
详细信息 查看全文
- 作者：Hamid Bostania ; st_h_bostani@azad.ac.ir" class="auth_mail" title="E-mail the corresponding author ; Mansour Sheikhanb ; 1 ; msheikhn@azad.ac.ir" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Optimum-path forest ; Classification ; Clustering ; Pruning ; Centrality ; Prestige ; Social network analysis
- 刊名：Pattern Recognition
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：62
- 期：Complete
- 页码：56-72
- 全文大小：1667 K
文摘
- class="listitem" id="list_li0005">
- class="label">•
We proposed a modified version of optimum-path forest (MOPF) for intrusion detection.
- class="label">•
Social network analysis is used for pruning the training set to speed up the OPF.
- class="label">•
A partitioning module is used to improve the detection rate of low-frequent attacks.
- class="label">•
The classification phase of traditional OPF is modified for improving the accuracy.
- class="label">•
Our method improved detection/false alarm rate and execution time of traditional OPF.