Intrusion Detection System In Iot Network Using Machine Learning

Since the last decade, Internet of Things (IoT - Internet of Things) solutions have been created and applied in different branches of society, such as solutions for transport and urban communication, for example. In this way, IoT is a new paradigm, which is composed of a global network of machines and devices capable of interacting with each other [4]. IoT changes the way some real-world problems can be modeled in the cyber environment, and this is due to the ability to list device units on the network to perform small tasks, and then group the results. In this work we have designed and implemented a system whose main purpose is to assist in the process of implementing this approach, a machine learning technique known as Active Learning was used. This technique was chosen in the context of this work, due to its potential to induce predictive models from databases with the lowest number of labels. Thus, a particle swarm algorithm PSO was used on the database, which consists of a supervised learning algorithm to validate the results obtained, a second predictive model was induced, which did not use active learning for sample selection. This model was used as a basis for comparative analysis of the model resulting from the proposal of this work.