Meta-Learner-Based Approach for Detecting Attacks on Internet of Things Networks

Abstract

The significant surge in Internet of Things (IoT) devices presents substantial challenges to network security. Hackers are afforded a larger attack surface to exploit as more devices become interconnected. Furthermore, the sheer volume of data these devices generate can overwhelm conventional security systems, compromising their detection capabilities. To address these challenges posed by the increasing number of interconnected IoT devices and the data overload they generate, this paper presents an approach based on meta-learning principles to identify attacks within IoT networks. The proposed approach constructs a meta-learner model by stacking the predictions of three Deep-Learning (DL) models: RNN, LSTM, and CNN. Subsequently, the identification by the meta-learner relies on various methods, namely Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). To assess the effectiveness of this approach, extensive evaluations are conducted using the IoT dataset from 2020. The XGBoost model showcased outstanding performance, achieving the highest accuracy (98.75%), precision (98.30%), F1-measure (98.53%), and AUC-ROC (98.75%). On the other hand, the SVM model exhibited the highest recall (98.90%), representing a slight improvement of 0.14% over the performance achieved by XGBoost.