Abstract
In many enterprises and the private sector, the Internet of Things (IoT) has spread globally. The growing number of different devices connected to the IoT and their various protocols have contributed to the increasing number of attacks, such as denial-of-service (DoS) and remote-to-local (R2L) ones. There are several approaches and techniques that can be used to construct attack detection models, such as machine learning, data mining, and statistical analysis. Nowadays, this technique is commonly used because it can provide precise analysis and results. Therefore, we decided to study the previous literature on the detection of IoT attacks and machine learning in order to understand the process of creating detection models. We also evaluated various datasets used for the models, IoT attack types, independent variables used for the models, evaluation metrics for assessment of models, and monitoring infrastructure using DevSecOps pipelines. We found 49 primary studies, and the detection models were developed using seven different types of machine learning techniques. Most primary studies used IoT device testbed datasets, and others used public datasets such as NSL-KDD and UNSW-NB15. When it comes to measuring the efficiency of models, both numerical and graphical measures are commonly used. Most IoT attacks occur at the network layer according to the literature. If the detection models applied DevSecOps pipelines in development processes for IoT devices, they were more secure. From the results of this paper, we found that machine learning techniques can detect IoT attacks, but there are a few issues in the design of detection models. We also recommend the continued use of hybrid frameworks for the improved detection of IoT attacks, advanced monitoring infrastructure configurations using methods based on software pipelines, and the use of machine learning techniques for advanced supervision and monitoring.
Highlights
The KDDCUP99, NSLKDD, and UNSW-NB15 datasets have been generated for evaluating intrusion detection systems (IDSs)
The neural networks (NNs) technique has been widely used to enhance the representation of data to build better models
We reviewed the performance of Internet of Things (IoT) attack detection models that use machine learning techniques to analyze and evaluate attacks
Summary
In 1999, Kevin Ashton used the term Internet of Things (“IoT”) for the first time in the supply chain management context, but it is used from a general perspective [1]. The. Internet of Things (IoT) includes infrastructures of systems, people, interconnected entities, and information resources integrated with services that manipulate information [2]. IoT systems are distributed dynamically and incorporate edge, cloud, and fog computing methods based on the allocation of information and computational resources [3]. IoT devices should cooperate with each other [4]. IoT devices communicate with each other through wireless communication systems and transfer information to a centralized system [5]
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