Document Type

Article

Publication Date

2025

DOI

10.1186/s40537-025-01329-w

Publication Title

Journal of Big Data

Volume

12

Issue

1

Pages

273 (1-38)

Abstract

The Internet of Urban Things (IoUTs) regularly generates large amounts of data, making it a focus of cyberthreats such as denial-of-service attacks and malware bot networks. Traditional intrusion detection systems struggle to detect intricate attack patterns, handle class imbalance, capture temporal dependencies, and exhibit transparency. To address these limitations, we introduce a novel deep machine learning model, DeepSecure, a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction and Deep Neural Networks for attack classification. DBN is used for feature selection through unsupervised learning to extract hierarchical representations in the IoUTs network data. We assess the random oversampling examples technique to improve model generalization and prevent class imbalance. DeepSecure is implemented using the TON_IoT dataset, which includes multi-source attack data indicative of Industry 4.0 cyber risks. In multi-class classification, DeepSecure shows improvement score of 21.25% in accuracy, recall, F1-score, and 18.29% in precision. Whereas, in binary classification, it increases accuracy by 11.23%, precision by 15.11%, F1-score by 10%, and recall by 5.32%. ANOVA T-test and 10-fold cross-validation are utilized for results validation to ensure DeepSecure’s reliability. Additionally, we use Shapley additive explanations to interpret the DeepSecure’s decision-making process to provide insight into feature contributions and model transparency. By effectively tackling IoUTs-specific cybersecurity challenges such as attack pattern detection, data imbalance, and lack of interpretability, the results demonstrate that DeepSecure is a practical, and transparent, solution for IoUTs network security.

Rights

© The Authors 2025.

This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source, provide a link to the Creative Commons license, and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Data Availability

Article states: "No datasets were generated or analysed during the current study."

Original Publication Citation

Sabir, L., Javaid, N., Akbar, M., Alrajeh, N., Bouk, S. H., & Aldegheishem, A. (2025). DeepSecure: A novel deep learning model for effective detection of attacks on big data in Internet of Urban Things. Journal of Big Data, 12(1), 1-38, Article 273. https://doi.org/10.1186/s40537-025-01329-w

ORCID

0000-0002-1764-7703 (Bouk)

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