Research Article
A data-driven approach for Network Intrusion Detection and Monitoring based on Kernel Null Space
@ARTICLE{10.4108/eai.13-6-2019.159801, author={Truong Thu Huong and Ta Phuong Bac and Quoc Thong Nguyen and Huu Du Nguyen and Kim Phuc Tran}, title={A data-driven approach for Network Intrusion Detection and Monitoring based on Kernel Null Space}, journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems}, volume={6}, number={20}, publisher={EAI}, journal_a={INIS}, year={2019}, month={8}, keywords={Network Security Support, Kernel Quantile Estimator, One-class Classification, Kernel Null Space vector machine}, doi={10.4108/eai.13-6-2019.159801} }- Truong Thu Huong
Ta Phuong Bac
Quoc Thong Nguyen
Huu Du Nguyen
Kim Phuc Tran
Year: 2019
A data-driven approach for Network Intrusion Detection and Monitoring based on Kernel Null Space
INIS
EAI
DOI: 10.4108/eai.13-6-2019.159801
Abstract
In this study, we propose a new approach to determine intrusions of network in real-time based on statistical process control technique and kernel null space method. The training samples in a class are mapped to a single point using the Kernel Null Foley-Sammon Transform. The Novelty Score are computed from testing samples in order to determine the threshold for the real-time detection of anomaly. The efficiency of the proposed method is illustrated over the KDD99 data set. The experimental results show that our new method outperforms the OCSVM and the original Kernel Null Space method by 1.53% and 3.86% respectively in terms of accuracy.
Copyright © 2019 Truong Thu Huong et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
