A machine learning-based lightweight intrusion detection system for the internet of things

被引：31

作者：

Fenanir S. ^{[1
]}

Semchedine F. ^{[2
]}

Baadache A. ^{[3
]}

机构：

[1] Department of Computer Science, Faculty of Exact Sciences, University of Bejaia, Bejaia

[2] Institute of Optics and Precision Mechanics (IOMP), University of Setif 1, Setif

[3] University of Alger 3, Algiers

来源：

Revue d'Intelligence Artificielle | 2019年 / 33卷 / 03期

关键词：

Anomaly detection; Feature selection; Internet of things (IoT); Intrusion detection system (IDS);

D O I：

10.18280/ria.330306

中图分类号：

学科分类号：

摘要：

The Internet of Things (IoT) is vulnerable to various attacks, due to the presence of tiny computing devices. To enhance the security of the IoT, this paper builds a lightweight intrusion detection system (IDS) based on two machine learning techniques, namely, feature selection and feature classification. The feature selection was realized by the filter-based method, thanks to its relatively low computing cost. The feature classification algorithm for our system was identified through comparison between logistic regression (LR), naive Bayes (NB), decision tree (DT), random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM) and multilayer perceptron (MLP). Finally, the DT algorithm was selected for our system, owing to its outstanding performance on several datasets. The research results provide a guide on choosing the optimal feature selection method for machine learning. © 2019 Lavoisier. All rights reserved.

引用

页码：203 / 211

页数：8

共 25 条

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