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An improved synergistic dual-layer feature selection algorithm with two type classifier for efficient intrusion detection in IoT environment
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An improved synergistic dual-layer feature selection algorithm with two type classifier for efficient intrusion detection in IoT environment
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Journal Article
An improved synergistic dual-layer feature selection algorithm with two type classifier for efficient intrusion detection in IoT environment
2025
Overview
In an era of increasing sophistication and frequency of cyber threats, securing Internet of Things (IoT) networks has become a paramount concern. IoT networks, with their diverse and interconnected devices, face unique security challenges that traditional methods often fail to address effectively. To tackle these challenges, an Intrusion Detection System (IDS) is specifically designed for IoT environments. This system integrates a multi-faceted approach to enhance security against emerging threats. The proposed IDS encompasses three critical subsystems: data pre-processing, feature selection and detection. The data pre-processing subsystem ensures high-quality data by addressing missing values, removing duplicates, applying one-hot encoding, and normalizing features using min-max scaling. A robust feature selection subsystem, employing Synergistic Dual-Layer Feature Selection (SDFC) algorithm, combines statistical methods, such as mutual information and variance thresholding, with advanced model-based techniques, including Support Vector Machine (SVM) with Recursive Feature Elimination (RFE) and Particle Swarm Optimization (PSO) are employed to identify the most relevant features. The classification subsystem employ two stage classifier namely LightGBM and XGBoost for efficient classification of the network traffic as normal or malicious. The proposed IDS is implemented in MATLAB by using TON-IoT dataset with various performance metrics. The experimental results demonstrate that the proposed SDFC method significantly enhances classifier performance, consistently achieving higher accuracy, precision, recall, and F1 scores compared to other existing methods.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
