Hybrid Federated Ensemble Learning Approach for Re-al-Time Distributed DDoS Detection in IIoT Edge Compu-ting Environment
DOI:
https://doi.org/10.55606/jeei.v5i1.5099Keywords:
DDoS Detection , Edge Computing, Ensemble Learning, Federated Learning, Industrial IoTAbstract
Development rapid from the Industrial Internet of Things ( IIoT ) and edge computing have revolutionize modern industry through distributed data processing with latency low . However , progress this also enlarges risk security cyber , in particular Distributed Denial of Service (DDoS) attacks can to disable operation industry that is critical . System Detection Conventional Intrusion (IDS) own limitations in matter scalability , data privacy , and capabilities generalization to environment Heterogeneous IIoT . For answer challenge said , research This propose A framework Hybrid Federated–Ensemble Learning (FL–EL) work to improve efficiency detection real -time DDoS attacks on networks IIoT edge -based . This model utilizing the Edge -IIoTset dataset which reflects pattern Then cross real in system industry . Federated learning is used For train the model collaborative across multiple edge nodes without need move data to center , so that guard data privacy . Each node performs training local using the basic model such as Random Forest (RF), XGBoost , and Support Vector Machine (SVM). Then , the central server do aggregation use ensemble techniques such as soft voting and stacking. The preprocessing process includes SMOTE technique and Z-score normalization for handle imbalance class and improve performance .Evaluation results show that This FL–EL hybrid approach capable reach performance high (F1-score > 99.5%) and significantly significant reduce level error positive as well as burden communication , compared with approach centralized . Framework this also shows ability detection fast with latency low , making it suitable For implementation in the system IIoT that requires resilience time real . Development advanced will covers Explainable AI integration for model interpretation and blockchain for secure and transparent logging .
References
Agarwal, A., Khari, M., & Singh, R. (2022). Detection of DDoS attack using deep learning model in cloud storage applications. Wireless Personal Communications, 1–21. https://doi.org/10.1007/s11277-022-09646-9
Alam, M., Shahid, M., & Mustajab, S. (2024). Security challenges for workflow allocation model in cloud computing environment: A comprehensive survey. The Journal of Supercomputing, 1–65. https://doi.org/10.1007/s11227-024-05642-2
Alghazzawi, D., Alghazzawi, D. M., Khan, R. A., & Khan, R. U. (2021). Efficient detection of DDoS attacks using a hybrid deep learning model with improved features selection. Applied Sciences, 11(24), 11634. https://doi.org/10.3390/app112411634
Amjad, A., Syed, A. R., & Syed, R. (2019). Detection and mitigation of DDoS attack in cloud computing using machine learning algorithm. EAI Endorsed Transactions on Scalable Information Systems, 6(23), e7. https://doi.org/10.4108/eai.13-7-2018.162806
Balasubramaniam, S., Anitha, R., & Vijayakumar, P. (2023). Optimization enabled deep learning based DDoS attack detection in cloud computing. International Journal of Intelligent Systems, 2023. https://doi.org/10.1155/2023/9673284
Chen, X., Xu, Y., Sun, Y., & Tang, L. (2022). Adaptive federated learning for edge computing. IEEE Transactions on Mobile Computing. https://doi.org/10.1109/TMC.2022.3170423
Cil, A. E., & Erol, M. (2021). Detection of DDoS attacks with feed forward based deep neural network models. Expert Systems with Applications, 169, 114520. https://doi.org/10.1016/j.eswa.2020.114520
Dinh, P. T., & Park, M. (2021). R-EDoS: Robust economic denial of sustainability detection in an SDN-based cloud through stochastic recurrent neural networks. IEEE Access, 9, 35057–35074. https://doi.org/10.1109/ACCESS.2021.3051573
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. https://doi.org/10.1207/s15516709cog1402_1
Katiravan, J., & S. P., S. (2024). Botnets attack detection in IoT devices using ensemble classifiers. International Research Journal of Multidisciplinary Technovation, 6(3), 274–295. https://doi.org/10.54392/irjmt24321
Khan, M. A., Javeed, D., & Qayyum, A. (2023). Lightweight hybrid IDS based on deep ensemble and federated learning. Computers & Security, 128, 103208. https://doi.org/10.1016/j.cose.2023.103208
Khempetch, T., & Wuttidittachotti, P. (2021). DDoS attack detection using deep learning. IAES International Journal of Artificial Intelligence, 10(2), 382. https://doi.org/10.11591/ijai.v10.i2.pp382-389
Kushwah, G. S., & Ranga, V. (2021). Optimized extreme learning machine for detecting DDoS attacks in cloud computing. Computers & Security, 105, 102260. https://doi.org/10.1016/j.cose.2021.102260
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2021). A survey on federated learning: The journey towards privacy preserving machine learning. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3050775
Meng, W., et al. (2020). Building a secure blockchain-based authentication and credentials management system. Future Generation Computer Systems, 103, 490–498. https://doi.org/10.1016/j.future.2019.09.003
Moustafa, N., & Slay, J. (2019). The TON_IoT datasets for AI-IoT applications. Sensors, 19(1), 65. https://doi.org/10.3390/s19010065
Potluri, S., et al. (2020). Detection and prevention mechanisms for DDoS attack in cloud computing environment. 2020 11th ICCCNT, IEEE, 1–6. https://doi.org/10.1109/ICCCNT49239.2020.9225520
Priyadarshini, R., & Barik, R. K. (2022). A deep learning based intelligent framework to mitigate DDoS attack in fog environment. Journal of King Saud University – Computer and Information Sciences, 34(3), 825–831. https://doi.org/10.1016/j.jksuci.2018.09.014
Rose, S., Borchert, O., Mitchell, S., & Connelly, S. (2020). Zero Trust Architecture (NIST SP 800-207). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-207
Sharafaldin, I., et al. (2019). Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy. 2019 International Carnahan Conference on Security Technology (ICCST), 1–8. https://doi.org/10.1109/CCST.2019.8888419
Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018). Towards generating a new intrusion detection dataset and intrusion traffic characterization. ICISSP, 108–116. https://doi.org/10.5220/0006639801080116
Sir, T. A., Kiran, R., & Kumar, R. (2020). Performance evaluation of Botnet DDoS attack detection using machine learning. Evolutionary Intelligence, 13(2), 283–294. https://doi.org/10.1007/s12065-019-00318-5
Songa, A. V., & Karri, G. R. (2023). Ensemble-RNN: A robust framework for DDoS detection in cloud environment. Assembly Journal of Electrical Engineering, 17(4), 31–44.
Sumathi, S., Rajalakshmi, P., & Rajasekar, R. (2022). Recurrent and deep learning neural network models for DDoS attack detection. Journal of Sensors, 2022. https://doi.org/10.1155/2022/3309575
Ur Rehman, S., Qamar, F., & Nazir, B. (2021). DIDDOS: An approach for detection and identification of Distributed Denial of Service (DDoS) cyberattacks using gated recurrent units (GRU). Future Generation Computer Systems, 118, 453–466. https://doi.org/10.1016/j.future.2020.12.006
Varma, P. R. K., R., R. S., & Vanitha, M. (2023). Enhanced Elman spike neural network based intrusion detection. Concurrency and Computation: Practice and Experience, 35(2), e7503. https://doi.org/10.1002/cpe.7503
Velliangiri, S., Ramya, R., & Sathya, R. (2021). Detection of distributed denial of service attack in cloud computing using the optimization-based deep networks. Journal of Experimental & Theoretical Artificial Intelligence, 33(3), 405–424. https://doi.org/10.1080/0952813X.2020.1719192
Wang, Y., Li, J., & Liu, Y. (2023). Edge-enhanced ensemble learning for anomaly detection in IIoT. Journal of Parallel and Distributed Computing. https://doi.org/10.1016/j.jpdc.2023.104759
Zhao, J., Wang, X., & Zhang, Y. (2023). Federated learning with dynamic aggregation for IoT security. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2023.3256564
Zhou, Y., Liu, C., & Zhang, M. (2022). Real-time DDoS detection using lightweight decision tree model in edge computing. Computer Networks, 208, 108879. https://doi.org/10.1016/j.comnet.2022.108879
.png)
.png)
