A novel framework for analyzing internet of things datasets for machine learning and deep learning-based intrusion detection systems

Muhammad Arief, Made Gunawan, Agung Septiadi, Mukti Wibowo, Vitria Pragesjvara, Kusnanda Supriatna, Anto Satriyo Nugroho, I Gusti Bagus Baskara Nugraha, Suhono Harso Supangkat


To generate a machine learning (ML) and deep learning (DL) architecture with good performance, we need a decent dataset for the training and testing phases of the development process. Starting with the knowledge discovery and data mining (KDD) Cup 99 dataset, numerous datasets have been produced since 1998 to be utilized in the ML and DL-based intrusion detection systems (IDS) training and testing process. Because there are so many datasets accessible, it might be challenging for researchers to choose which dataset to employ. Therefore, a framework for evaluating dataset appropriateness with the research to be conducted is becoming increasingly crucial as new datasets are regularly created. Additionally, given the growing popularity of internet of things (IoT) devices and an increasing number of specific datasets for IoT in recent years, it is essential to have a specific framework for IoT datasets. Therefore, this research aims to develop a new framework for evaluating IoT datasets for ML and DL-based IDS. The study's findings include, first, a novel framework for assessing IoT datasets, second, a comparison of this novel framework to other existing frameworks, and third, an analysis of five IoT datasets by using the new framework.


Cyber-attack; Deep learning; Internet of things dataset; Intrusion detection system; Machine learning

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DOI: http://doi.org/10.11591/ijai.v13.i2.pp1574-1584


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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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