Explainable AI for Lightweight Network Traffic Classification Using Depthwise Separable Convolutions
With the rapid growth of internet usage and the increasing number of connected devices, there is a critical need for advanced Network Traffic Classification (NTC) solutions to ensure optimal performance and robust security. Traditional NTC methods, such as port-based analysis and deep packet inspect...
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Computer Society |
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| Online Access: | https://ieeexplore.ieee.org/document/11023864/ |
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| author | Mustafa Ghaleb Mosab Hamdan Abdulaziz Y. Barnawi Muhammad Gambo Abubakar Danasabe Saheed Bello Aliyu Habib |
| author_facet | Mustafa Ghaleb Mosab Hamdan Abdulaziz Y. Barnawi Muhammad Gambo Abubakar Danasabe Saheed Bello Aliyu Habib |
| author_sort | Mustafa Ghaleb |
| collection | DOAJ |
| description | With the rapid growth of internet usage and the increasing number of connected devices, there is a critical need for advanced Network Traffic Classification (NTC) solutions to ensure optimal performance and robust security. Traditional NTC methods, such as port-based analysis and deep packet inspection, struggle to cope with modern network complexities, particularly dynamic port allocation and encrypted traffic. Recently, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been employed to develop classification models to accomplish this task. Existing models for NTC often require significant computational resources due to their large number of parameters, leading to slower inference times and higher memory consumption. To overcome these limitations, we introduce a lightweight NTC model based on Depthwise Separable Convolutions and compare its performance against CNN, RNN, and state-of-the-art models. In terms of computational efficiency, our proposed lightweight CNN exhibits a markedly reduced computational footprint. It utilizes only 30,611 parameters and 0.627 MFLOPS, achieving inference times of 1.49 seconds on the CPU and 0.43 seconds on the GPU. This corresponds to roughly 4× fewer FLOPS than the RNN baseline and 16× fewer than the CNN baseline, while also offering an ultracompact design compared to state-of-the-art models. Such efficiency makes it exceptionally well-suited for real-time applications in resource-constrained environments. In addition, we have integrated eXplainable Artificial Intelligence techniques, specifically LIME and SHAP, to provide valuable insights into model predictions. LIME and SHAP help interpret the contribution of each feature in decision-making, enhancing the transparency and trust in the model’s predictions, without compromising its lightweight nature. To support reproducibility and foster collaborative development, all associated code and resources have been made publicly available. |
| format | Article |
| id | doaj-art-d8d3502350f944ea89517d8870acbef6 |
| institution | Kabale University |
| issn | 2644-1268 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Computer Society |
| spelling | doaj-art-d8d3502350f944ea89517d8870acbef62025-08-20T03:29:35ZengIEEEIEEE Open Journal of the Computer Society2644-12682025-01-01690892010.1109/OJCS.2025.357649511023864Explainable AI for Lightweight Network Traffic Classification Using Depthwise Separable ConvolutionsMustafa Ghaleb0https://orcid.org/0000-0003-2842-6532Mosab Hamdan1https://orcid.org/0000-0002-1008-3028Abdulaziz Y. Barnawi2https://orcid.org/0000-0003-2364-6587Muhammad Gambo3https://orcid.org/0009-0009-2918-9357Abubakar Danasabe4https://orcid.org/0009-0000-7913-0462Saheed Bello5https://orcid.org/0000-0001-5606-3383Aliyu Habib6https://orcid.org/0009-0005-6673-962XIRC for Intelligent Secure Systems, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaSchool of Computing, National College of Ireland, Dublin, IrelandDepartment of Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaDepartment of Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaDepartment of Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaDepartment of Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaDepartment of Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaWith the rapid growth of internet usage and the increasing number of connected devices, there is a critical need for advanced Network Traffic Classification (NTC) solutions to ensure optimal performance and robust security. Traditional NTC methods, such as port-based analysis and deep packet inspection, struggle to cope with modern network complexities, particularly dynamic port allocation and encrypted traffic. Recently, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been employed to develop classification models to accomplish this task. Existing models for NTC often require significant computational resources due to their large number of parameters, leading to slower inference times and higher memory consumption. To overcome these limitations, we introduce a lightweight NTC model based on Depthwise Separable Convolutions and compare its performance against CNN, RNN, and state-of-the-art models. In terms of computational efficiency, our proposed lightweight CNN exhibits a markedly reduced computational footprint. It utilizes only 30,611 parameters and 0.627 MFLOPS, achieving inference times of 1.49 seconds on the CPU and 0.43 seconds on the GPU. This corresponds to roughly 4× fewer FLOPS than the RNN baseline and 16× fewer than the CNN baseline, while also offering an ultracompact design compared to state-of-the-art models. Such efficiency makes it exceptionally well-suited for real-time applications in resource-constrained environments. In addition, we have integrated eXplainable Artificial Intelligence techniques, specifically LIME and SHAP, to provide valuable insights into model predictions. LIME and SHAP help interpret the contribution of each feature in decision-making, enhancing the transparency and trust in the model’s predictions, without compromising its lightweight nature. To support reproducibility and foster collaborative development, all associated code and resources have been made publicly available.https://ieeexplore.ieee.org/document/11023864/Deep learningdepthwise separable convolutioneXplainable artificial intelligenceLIMEnetwork traffic classificationSHAP |
| spellingShingle | Mustafa Ghaleb Mosab Hamdan Abdulaziz Y. Barnawi Muhammad Gambo Abubakar Danasabe Saheed Bello Aliyu Habib Explainable AI for Lightweight Network Traffic Classification Using Depthwise Separable Convolutions IEEE Open Journal of the Computer Society Deep learning depthwise separable convolution eXplainable artificial intelligence LIME network traffic classification SHAP |
| title | Explainable AI for Lightweight Network Traffic Classification Using Depthwise Separable Convolutions |
| title_full | Explainable AI for Lightweight Network Traffic Classification Using Depthwise Separable Convolutions |
| title_fullStr | Explainable AI for Lightweight Network Traffic Classification Using Depthwise Separable Convolutions |
| title_full_unstemmed | Explainable AI for Lightweight Network Traffic Classification Using Depthwise Separable Convolutions |
| title_short | Explainable AI for Lightweight Network Traffic Classification Using Depthwise Separable Convolutions |
| title_sort | explainable ai for lightweight network traffic classification using depthwise separable convolutions |
| topic | Deep learning depthwise separable convolution eXplainable artificial intelligence LIME network traffic classification SHAP |
| url | https://ieeexplore.ieee.org/document/11023864/ |
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