An ensemble model for intrusion detection in the Internet of Softwarized Things

In recent years, there has been a rapid increase in the applications generating sensitive and personal information based on the Internet of Things (IoT). Due to the sensitive nature of the data there is a huge surge in intruders stealing the data from these applications. Hence a strong intrusion detection systems which can detect the intruders is the need of the hour to build a strong defence systems against the intruders. In this work, a Crow-Search based ensemble classifier is used to classify IoT- based UNSW-NB15 dataset. Firstly, the most significant features are selected from the dataset using Crow-Search algorithm, later these features are fed to the ensemble classifier based on Linear Regression, Random Forest and XGBoost algorithms for training. The performance of the proposed model is then evaluated against the state-of-the-art models to check for its effectiveness. The experimental results prove that the proposed model performs better than the other considered models.