Edge Computing Network Intrusion Detection System in IoT Using Deep Learning

A Network Intrusion Detection System (NIDS) is a technology to monitor the network traffic and detect and classify the possible cyber threats towards a computer network. In case it detects a potential cyberattack, it will alert the network prevent and defeat security system. Identifying the type of attack is invaluable for such system to block the malicious traffic or mitigate its impacts on the network. The Internet of Things (IoT) is a system of devices connected to the Internet, mainly for the purpose of acquiring data and transmitting that to the cloud, where the data will be processed, analyzed, and stored on the cloud and used by the user. In many cases, a group of IoT devices located at the same place form a network and send their data to the cloud through the network’s gateway. As IoT technology is rapidly growing, it is crucial to develop more accurate and efficient intrusion detection and classification systems. In this paper, we propose efficient deep learning models that classify the network traffic to benign vs seven main types of IoT attacks with a focus on CICIoT2023 dataset at the edge of IoT network by leveraging edge computing. This research addresses the challenges of applying deep learning on intrusion detection datasets, including data imbalance and data distribution discrepancy using data preprocessing techniques, such as random under sampling, class weighting, and feature scaling. We also study the impact of different feature selection techniques on the efficiencies of our models. Our experimental results showed that our 1D-CNN model outperforms the state-of-the-art with 93.8% F1-score.