Particle swarm optimization-enhanced machine learning and deep learning techniques for Internet of Things intrusion detection

Abstract

The exponential escalation of cyber threats and attacks targeting Internet of Things (IoT) devices in this decade necessitates the development of effective intrusion detection methods. This paper presents an innovative anomaly-based intrusion detection system that leverages machine learning (ML) and deep learning (DL) models to secure IoT networks. This study utilizes the RT_IoT2022 dataset, a novel dataset that captures complex IoT attack scenarios. The study implements particle swarm optimization (PSO), a bio-inspired metaheuristic, for feature selection and optimization, successfully reducing computational overhead while enhancing model performance. Several models, including the support vector machine, k-nearest neighbors, categorical boosting (CatBoost), naïve Bayes, convolutional neural network, and long short-term memory, have been evaluated for their ability to classify normal and malicious attacks. Our findings underscore the crucial roles of ML and DL in safeguarding IoT networks and the importance of continuous model evaluation using real-world data. Experiments demonstrate that CatBoost combined with PSO outperforms state-of-the-art methods in the literature on the same dataset across all metrics.

Topics & Concepts