Optimal Feature Extraction Model for Detecting Cyberattacks on IoT Devices

Internet of Things (IoT) is the technology of this modern era that focuses on connecting devices and sensors to the Internet and can converse with each other without human interaction.IoT technology has been used in many applications, such as smart and wearable devices automatically organizing people's appointments, and in many fields, such as communications in factories and companies.However, in contrast to its rapid spread, it faces several challenges.In terms of privacy, security, and confidentiality, IoT is vulnerable to many types of cyberattacks, making it necessary to develop safe solutions to secure IoT networks.In this paper, two feature extraction algorithms are integrated with three machine learning (ML) techniques to develop a model that detects a cyberattack faced by IoT devices.Butterfly optimization algorithm (BOA) and spider monkey optimization (SMO) with Na ve Bayes (NB), Random Forest (RF), and K-Nearest Neighbor (KNN) models are implemented.The experiment was conducted on the N-BaIoT dataset containing more than 800,000 records covering 10 IoT device attacks.The results show that the SMO feature extraction algorithm with the KNN classifier model outperformed other algorithms and achieved 100% in all performance metrics.