LAUSR

Before using any dataset in the intrusion detection system (IDS), it is crucial to acquire an accurate assessment of its efficiency. Nevertheless, the key complications presently met by the researchers are the deficiency of accessibility of any genuine assessment dataset and efficient metric for evaluating the enumerated quality of realism (QoR) of any internet of things (IoT)-based IDS dataset. It is challenging to obtain and gather data from real-world company setups owing to commercial continuousness and concerns such as integrity. This Letter presents a Sugeno fuzzy inference machine (SFIM)-based metric method for assessing the QoR of existing IoT IDS datasets. Secondly, based on the results of the proposed metric, a synthetically precise next level IoT-based IDS dataset is aimed and produced, and an initial assessment showed to support the development of forthcoming IoT-IDS. This created dataset comprises both regular and irregular replications of present IoT-based network events happening at the precarious cyber structure in different companies. Finally, the QoR of the generated dataset is evaluated by means of the proposed metric and is compared with state-of-the-art commonly available compelling datasets for validating its supremacy.