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Optimized Convolutional Neural Network Frameworks for Automatic Intrusion Detection Systems in Wireless Cybersecurity Applications

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Abstract

Securing the information traversing network infrastructures from increasingly sophisticated attackers is paramount. Cybersecurity has evolved to become a critical and integral component of any organizational information system. To this end, the development of Intrusion Detection Systems (IDSs) for monitoring, analyzing, and identifying malicious activities within network data flows is crucial. This paper introduces five innovative IDS models that leverage the capabilities of Convolutional Neural Networks (CNNs). The foremost model, termed the Three-layer Convolutional Neural Network (TCNN), has been designed to minimize parameters and computational demands, thereby expediting the model training process. Complementing this, four additional models have been adopted employing the principles of Transfer Learning (TL): VGG16, VGG19, ResNet50, and ResNet152. We have subjected these models to rigorous evaluation using the NSL-KDD dataset, encompassing four categories of cyber-attacks—Probe, Remote to User (R2U), User to Root (U2R), and Denial of Service (DoS)—as well as benign network behavior. Our models are adept at conducting both binary and multi-class classifications across diverse training and testing data proportions. They have achieved impeccable scores, with precision, recall, and F1-score each reaching 100%. Concurrently, the detection rate, classification accuracy, and false alarm rate stand at 99.99%, 99.81%, and 0.004, respectively. These outcomes corroborate the efficacy of the proposed systems in assisting cybersecurity professionals to efficiently pinpoint network breaches. Furthermore, we present a comparative analysis between our models and selected cutting-edge IDS frameworks to underscore their superior performance and dominance in the field.

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Acknowledgements

The authors are very grateful to all the institutions in the affiliation list for successfully performing this research work. The authors would like to thank Prince Sultan University for their support.

Funding

The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Youssef F. Sallam, Walid El-Shafai, Hossam El-din H. Ahmed, Adel Saleeb & Fathi E. Abd El-Samie

  2. Department of Artificial Intelligence Engineering, Faculty of Computer Science and Engineering, King Salman International University (KSIU), South Sinai, Egypt

    Samy Abd El-Nabi

  3. Automated Systems and Computing Lab (ASCL), Computer Science Department, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Walid El-Shafai

  4. Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Nirmeen A. El-Bahnasawy

  5. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

    Fathi E. Abd El-Samie

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  1. Youssef F. Sallam
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Correspondence to Walid El-Shafai.

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Sallam, Y.F., El-Nabi, S.A., El-Shafai, W. et al. Optimized Convolutional Neural Network Frameworks for Automatic Intrusion Detection Systems in Wireless Cybersecurity Applications. Wireless Pers Commun 143, 307–342 (2025). https://doi.org/10.1007/s11277-024-11535-z

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