Intrusion Detection in IoT leveraged by Multi-Access Edge Computing using Machine Learning
Dongjin Li, Nahid Ebrahimi Majd · 2023
An intrusion detection system is a technology built to monitor, detect, and prevent malicious activities and cyberattacks towards computer networks. The Internet of Things (IoT) is a system of devices connected to each other and to the Internet that facilitates communication between the IoT devices and the cloud. This rapidly growing technology calls for more accurate and efficient cyberattack detection techniques to ensure the security of IoT devices and systems. In this paper, we focus on using machine learning and deep learning algorithms along with feature selection methods to detect cyberattacks effectively at the edge of IoT network by leveraging multi-access edge computing. This study addresses the challenges of processing intrusion detection datasets, such as data imbalance and missing data, with a focus on UNSW-NB15 dataset. We use ANOVA and embedded feature selection techniques and apply various machine learning algorithms, consisting of Decision Tree (DT), Random Forest (RF), Light gradient-boosting machine (LightGBM), Artificial Neural Network (ANN), K-Nearest Neighbor (kNN), and Extreme Gradient Boost (XGB), on UNSW-NB15 dataset. Our experimental results indicate that our classifiers achieved higher accuracies and efficiencies comparing to state-of-the-art machine learning intrusion detection approaches and our LightGBM model is the most accurate and efficient one among all.