Network intrusion classification for IoT networks using an extreme learning machine

Abstract

The detection of intrusions has a significant impact on providing information security, and it is an essential technology to recognize diverse network threats effectively. This work proposes a machine learning technique to perform intrusion detection and classification using multiple feature extraction and testing using an Extreme learning machine (ELM). The model is evaluated on the two network intrusion datasets (NSL-KDD and UNSW-NB15), which consist of real-time network traffic. The arithmetic, gradient, and statistical features were extracted and evaluated with the proposed model. The method's efficacy is assessed using accuracy, sensitivity, specificity, precision, and F1-score. The proposed method achieves 94.5%, 97.61%, 96.91%, 96.51%, and 97.05% accuracy, sensitivity, specificity, precision, and F1-score for NSL-KDD and 94.3%, 98,36%, 99.31%, 99.67% and 99.01% of accuracy, sensitivity, specificity, precision and F1-score for the UNSWNB-15 dataset respectively, which is better performance outcomes when compared to other existing works.