PCM-RF a Hybrid Feature Selection Mechanism for Intrusion Detection System in IoT

The integrity and security of data must be protected in the framework of the Internet of Things (IoT). This article addresses the difficulties presented by possible cyber threats to IoT devices by introducing a unique feature selection technique called Pearson correlation matrix with random forest (PCM-RF). IoT device security is greatly influenced by machine learning techniques, which mostly depend on the caliber of characteristics taken from IoT datasets. By merging the advantages of RF with PCM, PCM-RF maximizes feature selection and fine-tunes features to improve the efficacy of current machine learning techniques and bolster classification algorithms' detecting powers. The study focuses on addressing limitations in existing ways of training and testing classification algorithms, which frequently lack strategies for optimizing and fine-tuning features. Thirty-four different forms of network assaults are included in the IoTCIC2023 dataset, used to assess PCM-RF. Results demonstrate PCM-RF's effectiveness, with XGBoost achieving an astounding accuracy of 99.39% and an 86% detection rate. Comparative studies highlight PCM-RF's superiority in detection and classification results, offering insights into IoT security and emphasizing its potential to improve the overall device robustness in the IoT ecosystem.