摘要：Almost all industrial internet of things (IIoT) attacks happen at the data transmission layeraccording to a majority of the sources. In IIoT, different machine learning (ML) and deep learning (DL)techniques are used for building the intrusion detection system (IDS) and models to detect the attacksin any layer of its architecture. In this regard, minimizing the attacks could be the major objective ofcybersecurity, while knowing that they cannot be fully avoided. The number of people resisting theattacks and protection system is less than those who prepare the attacks. Well-reasoned and learningbacked problems must be addressed by the cyber machine, using appropriate methods alongsidequality datasets. The purpose of this paper is to describe the development of the cybersecuritydatasets used to train the algorithms which are used for building IDS detection models, as well asanalyzing and summarizing the different and famous internet of things (IoT) attacks. This is carriedout by assessing the outlines of various studies presented in the literature and the many problemswith IoT threat detection. Hybrid frameworks have shown good performance and high detectionrates compared to standalone machine learning methods in a few experiments. It is the researchers’recommendation to employ hybrid frameworks to identify IoT attacks for the foreseeable future.
