lIDS-SIoEL: intrusion detection framework for IoT-based smart environments security using ensemble learning

Smart cities are being enabled all around the world by Internet of Things (IoT) applications. A smart city idea necessitates the integration of information and communication technologies and devices throughout a network in order to provide improved services to consumers. Because of their increasing amount and mobility, they are increasingly appealing to attackers. Therefore, several solutions, including as encryptions, authentication, availability, and data integrity, have been combined to protect IoT. Intrusion detection systems (IDSs) are a powerful security tool that may be improved by incorporating machine learning (ML) and deep learning (DP) techniques. This paper presents a novel intrusion detection framework for IoT-based smart environments with Ensemble Learning called IDS-SIoEL. Typically, the framework proposed an optimal anomaly detection model that uses AdaBoost, and combining different feature selection techniques Boruta, mutual information and correlation furthermore. The proposed model was evaluated on IoT-23, BoT-IoT, and Edge-IIoT datasets using the GPU. When compared to existing IDS, our approach provides good rating performance features of ACC, recall, and precision, with around 99.9% on record detection and calculation time of 33.68 s for learning and 0.02156 s for detection.

Automated summary

This paper presents a novel intrusion detection framework for IoT-based smart environments, which utilizes machine learning and deep learning techniques for improved protection. The framework demonstrates good performance in experiments and is capable of accurately detecting anomalies.