Intelligent Intrusion Detection System for Internet of Things using Machine Learning Techniques

Abstract

The Internet of Things (IoT) is an interconnected framework of computer devices, software, and sensors to work collectively to automate and monitor different tasks and procedures without human intervention. IoTs provide benefits and ease in performing operations for various industries and home and health stakeholders. Still, it faces security threats and challenges that make the IoT framework unreliable regarding security and privacy. Intrusion detection systems have been designed and deployed to overcome these security challenges. Over the past few years, for making the IDS intelligent artificial intelligence has been integrated with them. Besides this, different feature selection techniques are also being used to make the datasets appropriate for Machine Learning (ML) classifiers. This research uses a filter-based feature selection technique to select the important features from two datasets, UNSW-NB15 and NSL-KDD. After that, datasets with selected features are given to the classifiers as input. This work also evaluates different ML classifiers such as Multiple Layers Perceptron (MLP), CatBoost, Light Gradient Boosting (LGBM), Extra-Tree Classifier (ETC), Random Forest (RF), and K-nearest neighbor (KNN). Different metrics, Accuracy, Precision, Recall, F1, Detection Rate, and FAR, have been used for performance analysis. Evaluation results proved the LGBM classifier effective among other classifiers with 98.70% accuracy on the UNSW-NB15 dataset and 99.8% on the NSL-KDD dataset after feature selection with mean absolute difference (MAD) feature selection technique. Results are also compared with existing models to provide the efficiency of the proposed model.