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How to cite this paper
APA: Qtaish, A., Alieyan, K., Alkhasawneh, M., Alsmadi, I., Younes, M & Sawah, M. (2026). An explainable hybrid deep learning framework for binary intrusion detection with 5-fold stratified cross-validation. International Journal of Data and Network Science, 10(3), 1083-1098.
Chicago/Turabian: Qtaish, A., Alieyan, K., Alkhasawneh, M., Alsmadi, I., Younes, M & Sawah, M. 2026. "An explainable hybrid deep learning framework for binary intrusion detection with 5-fold stratified cross-validation." International Journal of Data and Network Science 10, no. 3 (2026): 1083-1098.
AMA: Qtaish, A., Alieyan, K., Alkhasawneh, M., Alsmadi, I., Younes, M & Sawah, M. An explainable hybrid deep learning framework for binary intrusion detection with 5-fold stratified cross-validation. International Journal of Data and Network Science. 2026;10(3):1083-1098.
References
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Abou El Houda, Z., Brik, B., & Khoukhi, L. (2022). “why should i trust your ids?”: An explainable deep learning framework for intrusion detection systems in internet of things networks. IEEE Open Journal of the Communications Society, 3, 1164-1176.
Al-E'mari, S., Anbar, M., Sanjalawe, Y., Manickam, S., & Hasbullah, I. (2022). Intrusion detection systems using blockchain technology: A review, issues and challenges. Computer Systems Science & Engineering, 40(1).
Alatawi, M. N. (2025). Enhancing intrusion detection systems with advanced machine learning techniques: an ensemble and explainable artificial intelligence (AI) approach. Security and Privacy, 8(1), e496.
Ali, M. L., Thakur, K., Schmeelk, S., Debello, J., & Dragos, D. (2025). Deep learning vs. machine learning for intrusion detection in computer networks: A comparative study. Applied Sciences, 15(4), 1903.
Almolhis, N. A. (2025). Intrusion Detection Using Hybrid Random Forest and Attention Models and Explainable AI Visualization. Journal of Internet Services and Information Security, 15(1), 371-384.
Amin, R., Costanzo, A., Alzabin, L. R., Aqdus, A., Kamarulzaman, S. F., & Alshehri, A. H. (2025). An efficient federated learning based defense mechanism for software defined network cyber threats through machine learning models. Scientific Reports, 15(1), 41390.
Arreche, O., Guntur, T., & Abdallah, M. (2024). Xai-ids: Toward proposing an explainable artificial intelligence framework for enhancing network intrusion detection systems. Applied Sciences, 14(10), 4170.
Arafah, M., Phillips, I., Adnane, A., Hadi, W., Alauthman, M., & Al-Banna, A. K. (2025). Anomaly-based network intrusion detection using denoising autoencoder and Wasserstein GAN synthetic attacks. Applied Soft Computing, 168, 112455.
Arya, K., Siddhant, S., & Upadhyay, L. (2025). An Explainable Hybrid Deep Learning Framework for Network Intrusion Detection Using Feature-Guided CNN Models. IEEE Access, 13, 204954-204977.
Attique, D., Hao, W., Ping, W., Javeed, D., & Kumar, P. (2024). Explainable and data-efficient deep learning for enhanced attack detection in IIoT ecosystem. IEEE Internet of Things Journal, 11(24), 38976-38986.
Balla, A., Habaebi, M. H., Elsheikh, E. A., Islam, M. R., Suliman, F. E. M., & Mubarak, S. (2024). Enhanced CNN-LSTM deep learning for SCADA IDS featuring Hurst parameter self-similarity. IEEE Access, 12, 6100-6116.
Gaspar, D., Silva, P., & Silva, C. (2024). Explainable AI for intrusion detection systems: LIME and SHAP applicability on multi-layer perceptron. IEEE Access, 12, 30164-30175.
Grabowski, A., & Xu, S. (2025). Advancing Cybersecurity Practice: Explainable Machine Learning for Network Intrusion Detection. Journal of Cybersecurity Education, Research and Practice, 2025(1), 25.
Halbouni, A., Gunawan, T. S., Habaebi, M. H., Halbouni, M., Kartiwi, M., & Ahmad, R. (2022). CNN-LSTM: hybrid deep neural network for network intrusion detection system. IEEE access, 10, 99837-99849.
Halimaa, A., & Sundarakantham, K. (2019, April). Machine learning based intrusion detection system. In 2019 3rd International conference on trends in electronics and informatics (ICOEI) (pp. 916-920). IEEE.
Javeed, D., Gao, T., Kumar, P., & Jolfaei, A. (2023). An explainable and resilient intrusion detection system for industry 5.0. IEEE Transactions on Consumer Electronics, 70(1), 1342-1350.
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning: C. Janiesch et al. Electronic markets, 31(3), 685-695.
Kamal, H., & Mashaly, M. (2024). Advanced hybrid transformer-CNN deep learning model for effective intrusion detection systems with class imbalance mitigation using resampling techniques. Future Internet, 16(12), 481.
Kamal, H., & Mashaly, M. (2025). Enhanced hybrid deep learning models-based anomaly detection method for two-stage binary and multi-class classification of attacks in intrusion detection systems. Algorithms, 18(2), 69.
Kalimuthu, N. (2025). Explainable AI (XAI) for Enhanced Cyber Threat Intelligence: Building Interpretable Intrusion Detection Systems. IJSAT-International Journal on Science and Technology, 16(4).
Keshk, M., Koroniotis, N., Pham, N., Moustafa, N., Turnbull, B., & Zomaya, A. Y. (2023). An explainable deep learning-enabled intrusion detection framework in IoT networks. Information Sciences, 639, 119000.
Khater, B. S., Abdul Wahab, A. W., Idris, M. Y. I., Hussain, M. A., Ibrahim, A. A., Amin, M. A., & Shehadeh, H. A. (2021). Classifier performance evaluation for lightweight IDS using fog computing in IoT security. Electronics, 10(14), 1633.
Kilincer, I. F. (2025). Explainable AI supported hybrid deep learnig method for layer 2 intrusion detection. Egyptian Informatics Journal, 30, 100669.
Kwubeghari, A., & Ezeji, N. G. (2025). Designing an Explainable Intrusion Detection System (X-Ids) Using Machine Learning: A Framework for Transparency and Trust. ABUAD Journal of Engineering Research and Development (AJERD), 8(2), 319-328.
Kus, D., Wagner, E., Pennekamp, J., Wolsing, K., Fink, I. B., Dahlmanns, M., ... & Henze, M. (2022, May). A false sense of security? Revisiting the state of machine learning-based industrial intrusion detection. In Proceedings of the 8th ACM on Cyber-Physical System Security Workshop (pp. 73-84).
Liu, Y., & Wu, L. (2023). Intrusion detection model based on improved transformer. Applied Sciences, 13(10), 6251.
Mallampati, S. B., & Seetha, H. (2024). Enhancing intrusion detection with explainable ai: A transparent approach to network security. Cybernetics and Information Technologies, 24(1), 98-117.
Mahmoud, M. M., Youssef, Y. O., & Abdel-Hamid, A. A. (2025). XI2S-IDS: An explainable intelligent 2-stage intrusion detection system. Future Internet, 17(1), 25.
Mohale, V. Z., & Obagbuwa, I. C. (2025). Evaluating machine learning-based intrusion detection systems with explainable AI: enhancing transparency and interpretability. Frontiers in Computer Science, 7, 1520741.
Nassar, M. O. (2025). A Robust and Energy-Efficient Federated IDS for IIoT Using Spiking Neural Networks and Differential Evolution with Adversarial Resilience. Iraqi Journal for Computer Science and Mathematics, 6(4), 3.
Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of big data, 2(1), 1.
Nugraha, B., Jnanashree, A. V., & Bauschert, T. (2025). A versatile XAI-based framework for efficient and explainable intrusion detection systems. Annals of Telecommunications, 80(11), 1095-1120.
Obagbuwa, I. C., Ngafeeson, M. N., Obagbuwa, O. F., & Tsetse, A. (2026). Machine Learning and Explainable Artificial Intelligence for Network Intrusion Detection. International Journal of Information Security and Privacy (IJISP), 20(1), 1-22.
Onyekwelu, O. M., Yanxia, S., & Mashao, D. (2025). Deep Learning-Based Intrusion Detection System: Embracing Long Short-Term Memory (LSTM) and Roughly Balanced Bagging Synergies. Inteligencia Artificial, 28(76), 40-65.
Pande, S., & Khamparia, A. (2023). Explainable deep neural network based analysis on intrusion detection systems. Computer Science, 24(1).
Patil, S., Varadarajan, V., Mazhar, S. M., Sahibzada, A., Ahmed, N., Sinha, O., ... & Kotecha, K. (2022). Explainable artificial intelligence for intrusion detection system. Electronics, 11(19), 3079.
Salloum, S., & Norozpour, S. (2025). XAI-IDS: A transparent and interpretable framework for robust cybersecurity using explainable artificial intelligence. Shifra, 2025, 69-80.
Sajid, M., Malik, K. R., Almogren, A., Malik, T. S., Khan, A. H., Tanveer, J., & Rehman, A. U. (2024). Enhancing intrusion detection: a hybrid machine and deep learning approach. Journal of Cloud Computing, 13(1), 123.
Sawah, M. S., Elmannai, H., El-Bary, A. A., Lotfy, K., & Sheta, O. E. (2025). Distributed denial of service (DDoS) classification based on random forest model with backward elimination algorithm and grid search algorithm. Scientific Reports, 15(1), 19063.
Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1(2018), 108-116.
Shtayat, M. B. M., Hasan, M. K., Sulaiman, R., Islam, S., & Khan, A. U. R. (2023). An explainable ensemble deep learning approach for intrusion detection in industrial internet of things. IEEE Access, 11, 115047-115061.
Srinivasulu, A., Kim, T. H., Chinthaginjala, R., Zhao, X., & Ahmad, I. (2025). Leveraging data analytics to revolutionize cybersecurity with machine learning and deep learning. Scientific Reports, 15(1), 31910.
Tariq, N., Alsirhani, A., Humayun, M., Alserhani, F., & Shaheen, M. (2024). A fog-edge-enabled intrusion detection system for smart grids. Journal of Cloud Computing, 13(1), 43.
Udofot, A. I., Oluseyi, O. M., & Bassey, E. (2024). Explainable AI for cyber security. Improving transparency and trust in intrusion detection systems. International Journal of Advances in Engineering and Management, 6(12), 229-240.
Ullah, F., Ullah, S., Srivastava, G., & Lin, J. C. W. (2024). IDS-INT: Intrusion detection system using transformer-based transfer learning for imbalanced network traffic. Digital Communications and Networks, 10(1), 190-204.
Wali, S., Farrukh, Y. A., & Khan, I. (2025). Explainable AI and random forest based reliable intrusion detection system. Computers & security, 157, 104542.
Wang, M., Zheng, K., Yang, Y., & Wang, X. (2020). An explainable machine learning framework for intrusion detection systems. IEEE access, 8, 73127-73141.
Wang, L., Zhang, Y., & Li, H. (2024). Explainable AI-based innovative hybrid ensemble model for intrusion detection systems. Journal of Cloud Computing: Advances, Systems and Applications, 13, 71.
Wang, Z. M., Tian, J. Y., Qin, J., Fang, H., & Chen, L. M. (2021). A Few‐Shot Learning‐Based Siamese Capsule Network for Intrusion Detection with Imbalanced Training Data. Computational intelligence and neuroscience, 2021(1), 7126913.
Wu, Z., Zhang, H., Wang, P., & Sun, Z. (2022). RTIDS: A robust transformer-based approach for intrusion detection system. IEEE access, 10, 64375-64387.
Yulianto, A., Sukarno, P., & Suwastika, N. A. (2019, March). Improving adaboost-based intrusion detection system (IDS) performance on CIC IDS 2017 dataset. In Journal of Physics: Conference Series (Vol. 1192, No. 1, p. 012018). IOP Publishing.
Zhang, Z., Kong, S., Xiao, T., & Yang, A. (2024). A network intrusion detection method based on bagging ensemble. Symmetry, 16(7), 850.
Abou El Houda, Z., Brik, B., & Khoukhi, L. (2022). “why should i trust your ids?”: An explainable deep learning framework for intrusion detection systems in internet of things networks. IEEE Open Journal of the Communications Society, 3, 1164-1176.
Al-E'mari, S., Anbar, M., Sanjalawe, Y., Manickam, S., & Hasbullah, I. (2022). Intrusion detection systems using blockchain technology: A review, issues and challenges. Computer Systems Science & Engineering, 40(1).
Alatawi, M. N. (2025). Enhancing intrusion detection systems with advanced machine learning techniques: an ensemble and explainable artificial intelligence (AI) approach. Security and Privacy, 8(1), e496.
Ali, M. L., Thakur, K., Schmeelk, S., Debello, J., & Dragos, D. (2025). Deep learning vs. machine learning for intrusion detection in computer networks: A comparative study. Applied Sciences, 15(4), 1903.
Almolhis, N. A. (2025). Intrusion Detection Using Hybrid Random Forest and Attention Models and Explainable AI Visualization. Journal of Internet Services and Information Security, 15(1), 371-384.
Amin, R., Costanzo, A., Alzabin, L. R., Aqdus, A., Kamarulzaman, S. F., & Alshehri, A. H. (2025). An efficient federated learning based defense mechanism for software defined network cyber threats through machine learning models. Scientific Reports, 15(1), 41390.
Arreche, O., Guntur, T., & Abdallah, M. (2024). Xai-ids: Toward proposing an explainable artificial intelligence framework for enhancing network intrusion detection systems. Applied Sciences, 14(10), 4170.
Arafah, M., Phillips, I., Adnane, A., Hadi, W., Alauthman, M., & Al-Banna, A. K. (2025). Anomaly-based network intrusion detection using denoising autoencoder and Wasserstein GAN synthetic attacks. Applied Soft Computing, 168, 112455.
Arya, K., Siddhant, S., & Upadhyay, L. (2025). An Explainable Hybrid Deep Learning Framework for Network Intrusion Detection Using Feature-Guided CNN Models. IEEE Access, 13, 204954-204977.
Attique, D., Hao, W., Ping, W., Javeed, D., & Kumar, P. (2024). Explainable and data-efficient deep learning for enhanced attack detection in IIoT ecosystem. IEEE Internet of Things Journal, 11(24), 38976-38986.
Balla, A., Habaebi, M. H., Elsheikh, E. A., Islam, M. R., Suliman, F. E. M., & Mubarak, S. (2024). Enhanced CNN-LSTM deep learning for SCADA IDS featuring Hurst parameter self-similarity. IEEE Access, 12, 6100-6116.
Gaspar, D., Silva, P., & Silva, C. (2024). Explainable AI for intrusion detection systems: LIME and SHAP applicability on multi-layer perceptron. IEEE Access, 12, 30164-30175.
Grabowski, A., & Xu, S. (2025). Advancing Cybersecurity Practice: Explainable Machine Learning for Network Intrusion Detection. Journal of Cybersecurity Education, Research and Practice, 2025(1), 25.
Halbouni, A., Gunawan, T. S., Habaebi, M. H., Halbouni, M., Kartiwi, M., & Ahmad, R. (2022). CNN-LSTM: hybrid deep neural network for network intrusion detection system. IEEE access, 10, 99837-99849.
Halimaa, A., & Sundarakantham, K. (2019, April). Machine learning based intrusion detection system. In 2019 3rd International conference on trends in electronics and informatics (ICOEI) (pp. 916-920). IEEE.
Javeed, D., Gao, T., Kumar, P., & Jolfaei, A. (2023). An explainable and resilient intrusion detection system for industry 5.0. IEEE Transactions on Consumer Electronics, 70(1), 1342-1350.
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning: C. Janiesch et al. Electronic markets, 31(3), 685-695.
Kamal, H., & Mashaly, M. (2024). Advanced hybrid transformer-CNN deep learning model for effective intrusion detection systems with class imbalance mitigation using resampling techniques. Future Internet, 16(12), 481.
Kamal, H., & Mashaly, M. (2025). Enhanced hybrid deep learning models-based anomaly detection method for two-stage binary and multi-class classification of attacks in intrusion detection systems. Algorithms, 18(2), 69.
Kalimuthu, N. (2025). Explainable AI (XAI) for Enhanced Cyber Threat Intelligence: Building Interpretable Intrusion Detection Systems. IJSAT-International Journal on Science and Technology, 16(4).
Keshk, M., Koroniotis, N., Pham, N., Moustafa, N., Turnbull, B., & Zomaya, A. Y. (2023). An explainable deep learning-enabled intrusion detection framework in IoT networks. Information Sciences, 639, 119000.
Khater, B. S., Abdul Wahab, A. W., Idris, M. Y. I., Hussain, M. A., Ibrahim, A. A., Amin, M. A., & Shehadeh, H. A. (2021). Classifier performance evaluation for lightweight IDS using fog computing in IoT security. Electronics, 10(14), 1633.
Kilincer, I. F. (2025). Explainable AI supported hybrid deep learnig method for layer 2 intrusion detection. Egyptian Informatics Journal, 30, 100669.
Kwubeghari, A., & Ezeji, N. G. (2025). Designing an Explainable Intrusion Detection System (X-Ids) Using Machine Learning: A Framework for Transparency and Trust. ABUAD Journal of Engineering Research and Development (AJERD), 8(2), 319-328.
Kus, D., Wagner, E., Pennekamp, J., Wolsing, K., Fink, I. B., Dahlmanns, M., ... & Henze, M. (2022, May). A false sense of security? Revisiting the state of machine learning-based industrial intrusion detection. In Proceedings of the 8th ACM on Cyber-Physical System Security Workshop (pp. 73-84).
Liu, Y., & Wu, L. (2023). Intrusion detection model based on improved transformer. Applied Sciences, 13(10), 6251.
Mallampati, S. B., & Seetha, H. (2024). Enhancing intrusion detection with explainable ai: A transparent approach to network security. Cybernetics and Information Technologies, 24(1), 98-117.
Mahmoud, M. M., Youssef, Y. O., & Abdel-Hamid, A. A. (2025). XI2S-IDS: An explainable intelligent 2-stage intrusion detection system. Future Internet, 17(1), 25.
Mohale, V. Z., & Obagbuwa, I. C. (2025). Evaluating machine learning-based intrusion detection systems with explainable AI: enhancing transparency and interpretability. Frontiers in Computer Science, 7, 1520741.
Nassar, M. O. (2025). A Robust and Energy-Efficient Federated IDS for IIoT Using Spiking Neural Networks and Differential Evolution with Adversarial Resilience. Iraqi Journal for Computer Science and Mathematics, 6(4), 3.
Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of big data, 2(1), 1.
Nugraha, B., Jnanashree, A. V., & Bauschert, T. (2025). A versatile XAI-based framework for efficient and explainable intrusion detection systems. Annals of Telecommunications, 80(11), 1095-1120.
Obagbuwa, I. C., Ngafeeson, M. N., Obagbuwa, O. F., & Tsetse, A. (2026). Machine Learning and Explainable Artificial Intelligence for Network Intrusion Detection. International Journal of Information Security and Privacy (IJISP), 20(1), 1-22.
Onyekwelu, O. M., Yanxia, S., & Mashao, D. (2025). Deep Learning-Based Intrusion Detection System: Embracing Long Short-Term Memory (LSTM) and Roughly Balanced Bagging Synergies. Inteligencia Artificial, 28(76), 40-65.
Pande, S., & Khamparia, A. (2023). Explainable deep neural network based analysis on intrusion detection systems. Computer Science, 24(1).
Patil, S., Varadarajan, V., Mazhar, S. M., Sahibzada, A., Ahmed, N., Sinha, O., ... & Kotecha, K. (2022). Explainable artificial intelligence for intrusion detection system. Electronics, 11(19), 3079.
Salloum, S., & Norozpour, S. (2025). XAI-IDS: A transparent and interpretable framework for robust cybersecurity using explainable artificial intelligence. Shifra, 2025, 69-80.
Sajid, M., Malik, K. R., Almogren, A., Malik, T. S., Khan, A. H., Tanveer, J., & Rehman, A. U. (2024). Enhancing intrusion detection: a hybrid machine and deep learning approach. Journal of Cloud Computing, 13(1), 123.
Sawah, M. S., Elmannai, H., El-Bary, A. A., Lotfy, K., & Sheta, O. E. (2025). Distributed denial of service (DDoS) classification based on random forest model with backward elimination algorithm and grid search algorithm. Scientific Reports, 15(1), 19063.
Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1(2018), 108-116.
Shtayat, M. B. M., Hasan, M. K., Sulaiman, R., Islam, S., & Khan, A. U. R. (2023). An explainable ensemble deep learning approach for intrusion detection in industrial internet of things. IEEE Access, 11, 115047-115061.
Srinivasulu, A., Kim, T. H., Chinthaginjala, R., Zhao, X., & Ahmad, I. (2025). Leveraging data analytics to revolutionize cybersecurity with machine learning and deep learning. Scientific Reports, 15(1), 31910.
Tariq, N., Alsirhani, A., Humayun, M., Alserhani, F., & Shaheen, M. (2024). A fog-edge-enabled intrusion detection system for smart grids. Journal of Cloud Computing, 13(1), 43.
Udofot, A. I., Oluseyi, O. M., & Bassey, E. (2024). Explainable AI for cyber security. Improving transparency and trust in intrusion detection systems. International Journal of Advances in Engineering and Management, 6(12), 229-240.
Ullah, F., Ullah, S., Srivastava, G., & Lin, J. C. W. (2024). IDS-INT: Intrusion detection system using transformer-based transfer learning for imbalanced network traffic. Digital Communications and Networks, 10(1), 190-204.
Wali, S., Farrukh, Y. A., & Khan, I. (2025). Explainable AI and random forest based reliable intrusion detection system. Computers & security, 157, 104542.
Wang, M., Zheng, K., Yang, Y., & Wang, X. (2020). An explainable machine learning framework for intrusion detection systems. IEEE access, 8, 73127-73141.
Wang, L., Zhang, Y., & Li, H. (2024). Explainable AI-based innovative hybrid ensemble model for intrusion detection systems. Journal of Cloud Computing: Advances, Systems and Applications, 13, 71.
Wang, Z. M., Tian, J. Y., Qin, J., Fang, H., & Chen, L. M. (2021). A Few‐Shot Learning‐Based Siamese Capsule Network for Intrusion Detection with Imbalanced Training Data. Computational intelligence and neuroscience, 2021(1), 7126913.
Wu, Z., Zhang, H., Wang, P., & Sun, Z. (2022). RTIDS: A robust transformer-based approach for intrusion detection system. IEEE access, 10, 64375-64387.
Yulianto, A., Sukarno, P., & Suwastika, N. A. (2019, March). Improving adaboost-based intrusion detection system (IDS) performance on CIC IDS 2017 dataset. In Journal of Physics: Conference Series (Vol. 1192, No. 1, p. 012018). IOP Publishing.
Zhang, Z., Kong, S., Xiao, T., & Yang, A. (2024). A network intrusion detection method based on bagging ensemble. Symmetry, 16(7), 850.