SCDF-CNN-BiGRU: إطار عمل لدمج الميزات المزدوجة المكانية والسياقية للكشف عن اختراقات الشبكات الهرمية متعددة مجموعات البيانات

Adefemi, K.O., M.B.M., and Alimi, O.A., 2025. A hybrid CNN–GRU deep learning model for IoT network intrusion detection. Journal of Sensor and Actuator Networks, 14(5), 96. https://doi.org/10.3390/jsan14050096

Akuthota, U.C., and Bhargava, L., 2025. Transformer-based intrusion detection for IoT networks. IEEE Internet of Things Journal, 12, pp. 6062–6067. https://doi.org/10.1109/JIOT.2025.3525494.

Alabbadi, A., and Bajaber, F., 2025. X-FuseRLSTM: A cross-domain explainable intrusion detection framework in IoT using the attention-guided dual-path feature fusion and residual LSTM. Sensors, 25(12), P. 3693. https://doi.org/10.3390/s25123693.

Alalwany, E., Alsharif, B., Alotaibi, Y., Alfahaid, A., Mahgoub, I., and Ilyas, M., 2025. Stacking ensemble deep learning for real-time intrusion detection in IoMT environments. Sensors, 25(3), P. 624. https://doi.org/10.3390/s25030624 .

Alayash, W., Rahrouh, M., Ibrahim, A.A., Mohamed, M.H., Ahmed, S.T., Albarri, M.H., and Ahmed, M.H., 2026. Assessing LSTM and GRU for multi-dataset intrusion detection in IoT environments. Statistics, Optimization & Information Computing, 15(4), pp. 3155–3173. https://doi.org/10.19139/soic-2310-5070-3226.

Ayantayo, A., Kaur, A., Schmid, A., and Waismann, B., 2023. Network intrusion detection using feature fusion with deep learning. Journal of Big Data, 10, P. 167. https://doi.org/10.1186/s40537-023-00834-0.

Bamber, S.S., Katkuri, A.V.R., Sharma, S. and Angurala, M., 2025. A hybrid CNN-LSTM approach for intelligent cyber intrusion detection system. Computers & Security, 148, P. 104146. https://doi.org/10.1016/j.cose.2024.104146.

Canadian Institute for Cybersecurity, 2025. UNSW-NB15 Augmented Dataset. University of New Brunswick. Available at: https://www.unb.ca/cic/datasets/cic-unsw-nb15.html [Accessed 27 June 2025].

Dai, W., Li, X., Ji, W., and He, S., 2024. Network intrusion detection method based on CNN, BiLSTM, and attention mechanism. IEEE Access, 12, pp. 53099–53111. https://doi.org/10.1109/ACCESS.2024.3385928.

Dhanabal, L., and Shantharajah, D.S.P., 2015. A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. https://doi.org/10.17148/IJARCCE.2015.4696 .

Gaspar, D., Antunes, A., and Bacao, F., 2024. Explainable AI for intrusion detection systems: LIME and SHAP applicability on multi-layer perceptron. IEEE Access, 12, pp. 30164–30178. https://doi.org/10.1109/ACCESS.2024.3368377.

Hussein, M.A., Alazawi, S.A.H., and Hoomod, H.K., 2026. Analysis of hybrid OS security using machine learning and deep learning. In: Advanced Engineering, Technology and Applications on Power Systems. Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-13921-4_47 .

Imrana, Y., Xiang, Y., Ali, L., Noor, A., Sarpong, K., and Abdullah, A. A., 2024. CNN-GRU-FF: A double-layer feature fusion-based network intrusion detection system using convolutional neural network and gated recurrent units. Complex & Intelligent Systems, 10, pp. 3353–3370. https://doi.org/10.1007/s40747-023-01313-y.

Kaur, J., Randhawa, S.K., and Singh, S., 2025. Ensemble learning approaches for multi-class intrusion detection systems for the Internet of Vehicles: A comprehensive survey. Future Internet, 17(7), P. 317. https://doi.org/10.3390/fi17070317.

Kavitha, P., and Harini, S.R., 2026. A dual-path hybrid deep learning framework with BiLSTM, multi-head attention, and intelligent feature engineering in IoT networks. Research Square Preprint. https://doi.org/10.21203/rs.3.rs-9302453/v1.

Li, B., Li, J., and Jia, M., 2025. ADFCNN-BiLSTM: A deep neural network based on attention and deformable convolution for network intrusion detection. Sensors, 25(5), P. 1382. https://doi.org/10.3390/s25051382.

Liu, L., and Xu, M., 2025. A network intrusion detection method based on contrastive learning and Bayesian Gaussian Mixture Model. Cybersecurity, 8(1), P. 59. https://doi.org/10.1186/s42400-025-00364-7 .

Liu, S., Yu, Y., Zong, Y., Yeoh, P.L., Guo, L., Vucetic, B., Duong, T.Q., and Li, Y., 2024. Delay and energy-efficient asynchronous federated learning for intrusion detection in heterogeneous industrial internet of things. IEEE Internet of Things Journal, 11(8), pp. 14739–14754. https://doi.org/10.1109/JIOT.2023.3335112.

Ma, X., Liang, J., and Yin, G., 2025. IoT intrusion detection technology based on DenseNet1D and Bi-GRU hybrid model. Discover Applied Sciences, 8(2), P. 115. https://doi.org/10.1007/s42452-025-08126-3 .

Mishra, S., Alshammari, N.S., Hussain, H., and Alfahidah, R.A., 2026. A cross-dataset harmonized intrusion detection framework with statistically validated multi-model learning. PLOS ONE, 21(4), P. e0346982. https://doi.org/10.1371/journal.pone.0346982 .

Mohammed, A.M., and Hoomod, H.K., 2026. A two-stage hybrid intrusion detection framework based on hierarchical attack mapping and pruned CNN-GRU models. Engineering, Technology & Applied Science Research, 16(1), pp. 32342–32347. https://doi.org/10.48084/etasr.16210.

Moustafa, N., and Slay, J., 2015. UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: 2015 Military Communications and Information Systems Conference (MilCIS), Australia, pp. 1–6. https://doi.org/10.1109/MilCIS.2015.7348942 .

Nandanwar, H., and Katarya, R., 2024. Deep learning enabled intrusion detection system for industrial IoT environment. Expert Systems with Applications, 249, P. 123808. https://doi.org/10.1016/j.eswa.2024.123808.

Nandanwar, H., and Katarya, R., 2025. Securing Industry 5.0: An explainable deep learning model for intrusion detection in cyber-physical systems. Computers & Electrical Engineering, 123, P. 110161. https://doi.org/10.1016/j.compeleceng.2025.110161.

Neto, E.C.P., Dadkhah, S., Ferreira, R., Zohourian, A., Lu, R., and Ghorbani, A.A., 2023. CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment. Sensors, 23(13), https://doi.org/10.3390/s23135941 .

Peng, H., Wu, C., and Xiao, Y., 2023. CBF-IDS: Addressing class imbalance using CNN-BiLSTM with focal loss in network intrusion detection system. Applied Sciences,13(21), P. 11629. https://doi.org/10.3390/app132111629.

Sagu, A., Gill, N.S., Gulia, P., Alduaiji, N., Shukla, P.K., and Shah, M.A., 2025. Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm. Scientific Reports, 15(1), P. 16485. https://doi.org/10.1038/s41598-025-99574-9.

Sarhan, M., Layeghy, S., Moustafa, N., and Portmann, M., 2021. NetFlow datasets for machine learning-based network intrusion detection systems. In: Deze, Z., Huang, H., Hou, R., Rho, S. and Chilamkurti, N., eds. Big Data Technologies and Applications. Cham: Springer. https://doi.org/10.1007/978-3-030-72802-1_9 .

Shafi, M., Lashkari, A.H., Rodriguez, V., and Nevo, R., 2024. Toward generating a new cloud-based distributed denial of service (DDoS) dataset and cloud intrusion traffic characterization. Information, 15(4), P. 195. https://doi.org/10.3390/info15040195 .

Sharma, S.B., and Bairwa, A.K., 2025. Leveraging AI for intrusion detection in IoT ecosystems: A comprehensive study. IEEE Access, 13, pp. 66290–66317. https://doi.org/10.1109/ACCESS.2025.3556701.

Thaljaoui, A., 2025. Intelligent network intrusion detection system using optimized deep CNN-LSTM with UNSW-NB15. International Journal of Information Technology. https://doi.org/10.1007/s41870-025-02416-0.

Wang, Z., Zainal, A., Siraj, M.M., Ghaleb, F.A., Hao, X., and Han, S., 2025. An intrusion detection model based on convolutional Kolmogorov-Arnold networks. Scientific Reports, 15(1), P. 1917. https://doi.org/10.1038/s41598-025-85700-4.

Xi, C., Wang, H., and Wang, X., 2024. A novel multi-scale network intrusion detection model with transformer. Scientific Reports, 14, P. 23239. https://doi.org/10.1038/s41598-024-74419-5.

Xu, H., Sun, L., Fan, G., Li, W., and Kuang, G., 2023. A hierarchical intrusion detection model combining multiple deep learning models with attention mechanism. IEEE Access, 11, pp. 66212–66226. https://doi.org/10.1109/ACCESS.2023.3290613.

Yang, K., Wang, J., and Li, M., 2024. An improved intrusion detection method for IIoT using attention mechanisms, BiGRU, and Inception-CNN. Scientific Reports, 14(1), P. 19339. https://doi.org/10.1038/s41598-024-70094-2.

Yaras, S., and Dener, M., 2024. IoT-based intrusion detection system using new hybrid deep learning algorithm. Electronics, 13, P. 1053. https://doi.org/10.3390/electronics13061053.

Zahid, M., and Bharati, T.S., 2025. Enhancing cybersecurity in IoT systems: A hybrid deep learning approach for real-time attack detection. Discover Internet of Things, 5, P. 73. https://doi.org/10.1007/s43926-025-00083-w.