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<p>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.</p>

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DeepSecure: a novel deep learning model for effective detection of attacks on big data in Internet of Urban Things

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月16日

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  • Laiba Sabir,
  • Nadeem Javaid,
  • Mariam Akbar,
  • Nabil Alrajeh,
  • Safdar Hussain Bouk,
  • Abdulaziz Aldegheishem

摘要

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.