Intelligent IoT security monitoring based on fuzzy optimum-path forest classifier

Abstract

The detection of intrusions in IoT networks is essential to maintain the availability and integrity of data transmitted and generated by devices connected to these networks. This is primarily when the data originates from critical activities, such as activities in the military, financial, industrial, and health sectors. Machine learning techniques have been adopted to create ways to detect or improve the accuracy of existing models for automatic intrusion detection. However, it is difficult to find in the literature an accurate intrusion detection technique in an IoT environment, as there are different types of attacks that can happen in different ways. Therefore, to solve this problem, this work proposes applying Fuzzy OPF (Optimum-Path Forest) as a new detection algorithm for any threat that escapes the regular traffic of an IoT network. We evaluate our proposed approach by using five different ML algorithms: Linear Discriminant Analysis, Support Vector Machine, Bayes, K-Nearest Neighbors, and Optimum-Path Forest. Experimental results analysis showed that our proposed model outperforms well-known algorithms in the literature regarding the Accuracy, Recall, and F1 metrics.

Prioritizing of The Internet of Manufacturing Things (IoMT) Challenges in Automotive Industry by using Interpretive Structural Modeling (ISM)

“…In recent years, in the eld of wireless communications and networking, a new paradigm called the Internet of Things (IoT) has attracted the attention of many researchers and industrialists. The Internet of Things can be de ned as a network of physical objects that are digitally connected so that they could sense, monitor, and in uence each other (Xu et al 2022). A supply chain is also a network that requires monitoring and control of relationships between components.…”