Open AccessGenerating realistic IoT‐based IDS dataset centred on fuzzy qualitative modelling for cyber‐physical systems
Author(s) -
Ahmed Khan Izhar,
Pi Dechang,
Bhatia Ajeet Kumar,
Khan Nasrullah,
Haider Waqas,
Wahab Abdul
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2019.4158
Subject(s) - computer science , metric (unit) , key (lock) , intrusion detection system , inference , internet of things , data mining , fuzzy logic , quality (philosophy) , fuzzy inference system , machine learning , artificial intelligence , data science , adaptive neuro fuzzy inference system , computer security , fuzzy control system , engineering , philosophy , operations management , epistemology
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.