An Approach for the Application of a Dynamic Multi-Class Classifier for Network Intrusion Detection Systems
Open Access
- 22 October 2020
- journal article
- research article
- Published by MDPI AG in Electronics
- Vol. 9 (11), 1759
- https://doi.org/10.3390/electronics9111759
Abstract
Currently, the use of machine learning models for developing intrusion detection systems is a technology trend which improvement has been proven. These intelligent systems are trained with labeled datasets, including different types of attacks and the normal behavior of the network. Most of the studies use a unique machine learning model, identifying anomalies related to possible attacks. In other cases, machine learning algorithms are used to identify certain type of attacks. However, recent studies show that certain models are more accurate identifying certain classes of attacks than others. Thus, this study tries to identify which model fits better with each kind of attack in order to define a set of reasoner modules. In addition, this research work proposes to organize these modules to feed a selection system, that is, a dynamic classifier. Finally, the study shows that when using the proposed dynamic classifier model, the detection range increases, improving the detection by each individual model in terms of accuracy.This publication has 18 references indexed in Scilit:
- Empirical study on multiclass classification‐based network intrusion detectionComputational Intelligence, 2019
- A Survey of Deep Learning Methods for Cyber SecurityInformation, 2019
- Automated Algorithm Selection: Survey and PerspectivesEvolutionary Computation, 2019
- CorrCorr: A feature selection method for multivariate correlation network anomaly detection techniquesComputers & Security, 2019
- Dynamic Autoselection and Autotuning of Machine Learning Models for Cloud Network AnalyticsIEEE Transactions on Parallel and Distributed Systems, 2018
- A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion DetectionIEEE Communications Surveys & Tutorials, 2018
- UGR‘16: A new dataset for the evaluation of cyclostationarity-based network IDSsComputers & Security, 2018
- Multi-classification approaches for classifying mobile app trafficJournal of Network and Computer Applications, 2018
- ASlib: A benchmark library for algorithm selectionArtificial Intelligence, 2016
- The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data setInformation Security Journal: A Global Perspective, 2016