Two-Dimensional Projection Based Wireless Intrusion Classification Using Lightweight EfficientNet

Abstract

Internet of Things (IoT) networks leverage wireless communication protocol, which adversaries can exploit. Impersonation attacks, injection attacks, and flooding are several examples of different attacks existing in Wi-Fi networks. Intrusion Detection System (IDS) became one solution to distinguish those attacks from benign traffic. Deep learning techniques have been intensively utilized to classify the attacks. However, the main issue of utilizing deep learning models is projecting the data, notably tabular data, into image-based data. This study proposes a novel projection from wireless network attacks data into grid-like data for feeding one of the Convolutional Neural Network (CNN) models, EfficientNet. We define the particular sequence of placing the attribute values in a matrix that would be captured as an image. By combining the most important subset of attributes and EfficientNet, we aim for an accurate and lightweight IDS module deployed in IoT networks. We examine the proposed model using the Wi-Fi attacks dataset, called AWID dataset. We achieve the best performance by a 99.91% F1 score and 0.11% false positive rate. In addition, our proposed model achieved comparable results with other statistical machine learning models, which shows that our proposed model successfully exploited the spatial information of tabular data to maintain detection accuracy. We also successfully maintain the false positive rate of about 0.11%. We also compared the proposed model with other machine learning models, and it is shown that our proposed model achieved comparable results with the other three models. We believe the spatial information must be considered by projecting the tabular data into grid-like data.