Abstract
Detection of intrusions in Internet of Things networks is essential to maintain the availability and integrity of the data generated and transmitted by connected devices. Such a procedure is paramount when the data originate from critical activities, such as military, financial, industrial, and health sectors. In the last decades, machine learning (ML)-based approaches have become one of the most suitable and adopted procedures for the task, providing automatic, fast, and accurate results. Despite such success, the literature still presents a gap regarding valid applications of intrusion detection in the IoT environments, which usually stands for a challenging task composed of different types of attacks. In this context, this work applies a recent technique based on graphs and logic fuzzy, namely Fuzzy Optimum-Path Forest (Fuzzy OPF), to detect threats that escape an IoT network’s regular traffic. We evaluate our model against five well-known ML algorithms, i.e., Linear Discriminant Analysis, Support Vector Machine, Naive Bayes, K-Nearest Neighbors, and the standard Optimum-Path Forest. Experimental results show that Fuzzy OPF outperforms the baselines considering accuracy, recall, and F1 metrics. As a result, the Fuzzy OPF proposal for intrusion detection had a hit rate of 98 and 99%.
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Enquiries about data availability should be directed to the authors.
Notes
Notice \(\mathcal{T}^*\) is obtained after calculating the selection of nearby samples that have different labels and after performing the Minimum Spanning Tree.
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Funding
This study was financed in part by the Science and Technology Planning Project of Guangdong Province (Grant No. 2018A050506086), by Research Start-up Funds of DGUT (GC300502-60), by the KEY Laboratory of Robotics and Intelligent Equipment of Guangdong Regular Institutions of Higher Education (Grant No. 2017KSYS009).
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Communicated by Deepak kumar Jain.
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Xu, Y., de Souza, R.W.R., Medeiros, E.P. et al. Intelligent IoT security monitoring based on fuzzy optimum-path forest classifier. Soft Comput 27, 4279–4288 (2023). https://doi.org/10.1007/s00500-022-07350-y
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DOI: https://doi.org/10.1007/s00500-022-07350-y