Abstract
The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols. The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able to enhance everyday tasks and facilitate smart decisions based on sensed data. The
Highlights
The Internet of Things (IoT) is a technology trend able to provide new features and services
The contribution of this paper, relative to the recent literature in the field, can be summarized as follows: i) The scope of this survey is different from other survey papers published in the field, i.e., this paper aimed to put emphasis on the used intrusion detection techniques based on machine learning for IoT networks. ii) This paper provided an overview of the related research work. iii) This paper studied the IoT network security issues and pointed out the necessity of intrusion detection system as a solution for detecting anomalies in IoT networks traffic. iv) This review paper compared the intrusion detection techniques and machine learning approaches. v) This paper provided taxonomies for attacks and anomalies detection schema
IoT systems have some security flaws and vulnerabilities, the commonest of which is that when attackers may misuse this emerging technology to threaten users’ privacy
Summary
The Internet of Things (IoT) is a technology trend able to provide new features and services. Thanks to its ubiquitous and pervasive fashion, cloud computing is an efficient solution for IoT data management and monitoring. It provides shared resources such as storage, computing and application via a cloud services platform connected to the Internet. IDS developed for IoT could face the challenge of determining the attack or the malicious partners They are classified mainly into three categories depending on the used detection methods: Anomaly detection, Misuse detection, and Hybrid detection.
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