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

Symmetry

Ahmed Adnan¹  Abdullah Muhammed¹  Fahrul Hakim¹  Azizol Abdullah¹  Abdul Azim Abd Ghani² 

[1] Department of Communication Technology and Networks, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang 43300, Malaysia;Department of Software Engineering and Information System, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang 43300, Malaysia;

关键词: intrusion detection system;    concept drift;    high dimensionality;    computational complexity;   

DOI  :  10.3390/sym13061011

来源: DOAJ

【 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.

【 授权许可】

Unknown