SVM Based Intrusion Detection Method with Nonlinear Scaling and Feature Selection
- 1 May 2022
- journal article
- research article
- Published by Institute of Electronics, Information and Communications Engineers (IEICE) in IEICE Transactions on Information and Systems
- Vol. E105.D (5), 1024-1038
- https://doi.org/10.1587/transinf.2021edp7184
Abstract
Intrusion is one of major security issues of internet with the rapid growth in smart and Internet of Thing (IoT) devices, and it becomes important to detect attacks and set out alarm in IoT systems. In this paper, the support vector machine (SVM) and principal component analysis (PCA) based method is used to detect attacks in smart IoT systems. SVM with nonlinear scheme is used for intrusion classification and PCA is adopted for feature selection on the training and testing datasets. Experiments on the NSL-KDD dataset show that the test accuracy of the proposed method can reach 82.2% with 16 features selected from PCA for binary-classification which is almost the same as the result obtained with all the 41 features; and the test accuracy can achieve 78.3% with 29 features selected from PCA for multi-classification while 79.6% without feature selection. The Denial of Service (DoS) attack detection accuracy of the proposed method can achieve 8.8% improvement compared with existing artificial neural network based method.Keywords
This publication has 37 references indexed in Scilit:
- Improving the Intrusion Detection using Discriminative Machine Learning Approach and Improve the Time Complexity by Data Mining Feature Selection MethodsInternational Journal of Computer Applications, 2013
- Intrusion detection system: A comprehensive reviewJournal of Network and Computer Applications, 2013
- A comparative study on the effect of feature selection on classification accuracyProcedia Technology, 2012
- Empirical study of feature selection methods based on individual feature evaluation for classification problemsExpert Systems with Applications, 2011
- LIBSVMACM Transactions on Intelligent Systems and Technology, 2011
- Intrusion detection by machine learning: A reviewExpert Systems with Applications, 2009
- Single-packet IP tracebackIEEE/ACM Transactions on Networking, 2002
- An introduction to intrusion detectionXRDS: Crossroads, The ACM Magazine for Students, 1996
- Support-vector networksMachine Learning, 1995
- Statistics notes: Calculating correlation coefficients with repeated observations: Part 2--correlation between subjectsBMJ, 1995