Scatter Plot–Based Tabular-to-Image Conversion Method with a Modified VGG16 Model for Intrusion Detection in IoT Networks
Year: 2026
Type: article
Abstract: Security is one of the most severe problems in the Internet of Things (IoT) environment. Early detection of attacks is crucial, as it can significantly enhance network security. Convolutional neural networks (CNNs) represent a significant advancement in image classification; however, the application of CNNs to the classification of tabular data remains less explored for Intrusion Detection Systems (IDSs) compared to their success in image-based tasks. In this paper, we develop and evaluate a novel method for the classification of data using a modified VGG16 model by transforming tabular data into images. This transformation handles each block of samples as an image. A modified VGG16 model is then trained to classify the transformed images. We applied this method to the CICIoT2023 cybersecurity dataset, and the model achieved an accuracy of 98.05% with precision, recall, and F1-score of 98.15%, 98.05%, and 98.04%, respectively. The experimental results show that the proposed method achieves superior detection performance and greater classification robustness compared to other state-of-the-art methods. The significance of this study lies in detecting intrusions in IoT networks effectively and efficiently by utilizing tabular to image transformation and VGG16 model.
Institution: Abbès Laghrour University of Khenchela
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7222885524