PCA and PSO based optimized support vector machine for efficient intrusion detection in internet of things

The Internet of Things (IoT) is a network that interconnects many everyday objects, including computers, televisions, washing machines, and even whole urban areas. These devices has the capability to collect and disseminate information because to their integration of electronics, software, sensors, and connectivity to a network. The Internet of Things enables the remote sensing, identification, and control of physical things via the utilisation of existing network infrastructure. By using this function, it becomes feasible to integrate elements of the physical world into computerised systems, resulting in enhanced levels of efficiency, precision, and financial profitability. The Internet of Things (IoT) encompasses a diverse array of applications. The Internet of Things (IoT) may be used in several sectors such as healthcare, smart cities, smart homes, transportation, logistics, agriculture, and smart traffic management. The quantity of Internet of Things (IoT) devices is increasing rapidly and exponentially. The surge in numbers is accompanied by a significant escalation in security vulnerabilities. This article presents the development of an intrusion detection system for the Internet of Things using machine learning and feature selection techniques. The system aims to accurately categorise and forecast attacks on IoT devices. This approach utilises the publicly accessible NSL KDD dataset as its input dataset. During the data collecting process for NSL-KDD, all symbolic qualities are transformed into their corresponding numerical representations. Conversely, all numerical features are translated back into symbolic form at the conclusion of the procedure. Principal component analysis is employed to achieve the objective of attribute extraction. After completing the preparation step, the data set is classified using several machine learning techniques such as support vector machine, linear regression, and random forest. Evaluating the veracity, exactness, and retrieval rate of different machine learning algorithms is crucial for choosing the most effective ones. The accuracy of the Intrusion Detection System (IDS) based on Particle Swarm Optimisation (PSO) is 98.5 percent. The PSO-based SVM method is shown superior performance compared to random forest and linear regression methods in terms of precision, recall, and specificity.