An Intrusion Detection System for the Internet of Things Based on Machine Learning: Review and Challenges

被引：38

作者：

Adnan, Ahmed ^{[1
]}

Muhammed, Abdullah ^{[1
]}

Abd Ghani, Abdul Azim ^{[2
]}

Abdullah, Azizol ^{[1
]}

Hakim, Fahrul ^{[1
]}

机构：

[1] Univ Putra Malaysia, Fac Comp Sci & Informat Technol, Dept Commun Technol & Networks, Serdang 43300, Malaysia

[2] Univ Putra Malaysia, Fac Comp Sci & Informat Technol, Dept Software Engn & Informat Syst, Serdang 43300, Malaysia

来源：

SYMMETRY-BASEL | 2021年 / 13卷 / 06期

关键词：

intrusion detection system; concept drift; high dimensionality; computational complexity; MICRO-CLUSTERS; ALGORITHM; SELECTION; STREAM; CLASSIFICATION; SCHEME;

D O I：

10.3390/sym13061011

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

An intrusion detection system (IDS) is an active research topic and is regarded as one of the important applications of machine learning. An IDS is a classifier that predicts the class of input records associated with certain types of attacks. In this article, we present a review of IDSs from the perspective of machine learning. We present the three main challenges of an IDS, in general, and of an IDS for the Internet of Things (IoT), in particular, namely concept drift, high dimensionality, and computational complexity. Studies on solving each challenge and the direction of ongoing research are addressed. In addition, in this paper, we dedicate a separate section for presenting datasets of an IDS. In particular, three main datasets, namely KDD99, NSL, and Kyoto, are presented. This article concludes that three elements of concept drift, high-dimensional awareness, and computational awareness that are symmetric in their effect and need to be addressed in the neural network (NN)-based model for an IDS in the IoT.

引用

页数：13

共 59 条

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