Intrusion Detection System: An Ensemble Deep Learning Approach for Cloud Computing Using EBWO
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Abstract
Cloud computing is the industry standard for data storage, sharing, processing, and other services. It experienced numerous security problems as a result of the regular attacks. These security issues are worsened by the variety of attack situations that exist. One of the most established safety measures applied to cloud computing is the intrusion detection system (IDS). An effective security model is necessary for the IDS system, though, to increase cloud security. In this study, we used ensemble categorization methods and a feature selection algorithm to construct an effective IDS for the cloud environment. The proposed BOT-IOT, CSE-CIC-IDS 2018, and Ciciddos datasets were pre-processed, which involved cleaning the data, applying one hot encoding, and normalizing steps. The Enhanced Black Widow Optimization (EBWO) algorithm was employed to choose the most advantageous reduced feature sets from the provided incursion datasets. We used an ensemble of Hierarchical Multi-scale LSTM (HMLSTM) and Darknet Convolutional Neural Network (DNetCNN) to categorize the attacks. The combination of DNetCNN and HMLSTM was used to identify intrusions, effectively classifying attacks, lowering false alarm rates, and increasing detection rates. Simulation research showed that the proposed strategy performed better than the baseline in terms of F-Score, DR, and FPR, as well as accuracy, detection rate, and precision.
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References
Alqahtani, H., & Kumar, G. (2022). A deep learning-based intrusion detection system for in-vehicle networks. Computers and Electrical Engineering, 10(4), Article 108447. https://doi.org/10.1016/j.compeleceng.2022.108447
Azzaoui, H., Boukhamla, A. Z. E., Arroyo, D., & Bensayah, A. (2022). Developing new deep-learning model to enhance network intrusion classification. Evolving Systems, 13(1), 17-25. https://doi.org/10.1007/s12530-020-09364-z
Babu, K. S., & Rao, Y. N. (2023). MCGAN: Modified conditional generative adversarial network (MCGAN) for class imbalance problems in network intrusion detection. Applied Sciences, 13(4), Article 2576. https://doi.org/10.3390/app13042576
Chiba, Z., Abghour, N., Moussaid, K., El Omri, A., & Rida, M. (2019). New anomaly network intrusion detection system in cloud environment based on optimized back propagation neural network using improved genetic algorithm. International Journal of Communication Networks and Information Security, 11(1), 61-84. https://doi/org/10.17762/ijcnis.v11i1.3764
Devan, P., & Khare, N. (2020). An efficient XGBoost–DNN-based classification model for network intrusion detection system. Neural Computing and Applications, 32(16), 12499-12514. https://doi.org/10.1007/s00521-020-04708-x
Du, J., Yang, K., Hu, Y., & Jiang, L. (2023). NIDS-CNNLSTM: Network intrusion detection classification model based on deep learning. IEEE Access, 11(1), 24808-24821. https://doi.org/10.1109/ACCESS.2023.3254915
Gupta, S. K., Tripathi, M., & Grover, J. (2022). Hybrid optimization and deep learning-based intrusion detection system. Computers and Electrical Engineering, 100(1), Article 107876. https://doi.org/10.1016/j.compeleceng.2022.108156
Hnamte, V., & Hussain, J. (2023). DCNNBiLSTM: An efficient hybrid deep learning-based intrusion detection system. Telematics and Informatics Reports, 10(1), Article 100053. https://doi.org/10.1016/j.teler.2023.100053
Imran, M., Haider, N., Shoaib, M., & Razzak, I. (2022). An intelligent and efficient network intrusion detection system using deep learning. Computers and Electrical Engineering, 9(9), Article 107764. https://doi.org/10.1016/j.compeleceng.2022.107764
Kasongo, S. M. (2023). A deep learning technique for intrusion detection system using a Recurrent Neural Networks-based framework. Computer Communications, 19(9), 113-125. https://doi.org/10.1016/j.comcom.2022.12.010
Khan, M. A. (2021). HCRNNIDS: hybrid convolutional recurrent neural network-based network intrusion detection system. Processes, 9(5), Article 834. https://doi.org/10.3390/pr9050834
Kim, T., & Pak, W. (2022). Robust network intrusion detection system based on machine-learning with early classification. IEEE Access, 10(1), 10754-10767. https://doi.org/ 10.1109/ACCESS.2022.3145002
Kunang, Y. N., Nurmaini, S., Stiawan, D., & Suprapto, B. Y. (2021). Attack classification of an intrusion detection system using deep learning and hyperparameter optimization. Journal of Information Security and Applications, 5(8), Article 102804. https://doi.org/10.1016/j.jisa.2021.102804
Liu, J., Gao, Y., & Hu, F. (2021). A fast network intrusion detection system using adaptive synthetic oversampling and LightGBM. Computers & Security, 10(6), Article 102289. https://doi.org/10.1016/j.cose.2021.102289
Mighan, S. N., & Kahani, M. (2021). A novel scalable intrusion detection system based on deep learning. International Journal of Information Security, 20(3), 387-403. https://doi.org/10.1007/s10207-020-00508-5
Rahman, M. A., Asyhari, A. T., Leong, L. S., Satrya, G. B., Tao, M. H., & Zolkipli, M. F. (2020). Scalable machine learning-based intrusion detection system for IoT-enabled smart cities. Sustainable Cities and Society, 6(1), Article 102324. https://doi.org/10.1016/j.scs.2020.102324
Ravi, V., Chaganti, R., & Alazab, M. (2022). Recurrent deep learning-based feature fusion ensemble meta-classifier approach for intelligent network intrusion detection system. Computers and Electrical Engineering, 10(2), Article 108156. https://doi.org/10.1016/j.compeleceng.2022.108156
Saba, T., Rehman, A., Sadad, T., Kolivand, H., & Bahaj, S. A. (2022). Anomaly-based intrusion detection system for IoT networks through deep learning model. Computers and Electrical Engineering, 99(1), Article 107810. https://doi.org/10.1016/j.compeleceng.2022.107810
Saheed, Y. K., Abiodun, A. I., Misra, S., Holone, M. K., & Colomo-Palacios, R. (2022). A machine learning-based intrusion detection for detecting internet of things network attacks. Alexandria Engineering Journal, 61(12), 9395-9409. https://doi.org/10.1016/j.aej.2022.02.063
Soltani, M., Ousat, B., Siavoshani, M. J., & Jahangir, A. H. (2023). An adaptable deep learning-based intrusion detection system to zero-day attacks. Journal of Information Security and Applications, 7(6), Article 103516. https://doi.org/10.1016/j.jisa.2023.103516
Thakkar, A., & Lohiya, R. (2021). Analyzing fusion of regularization techniques in the deep learning‐based intrusion detection system. International Journal of Intelligent Systems, 36(12), 7340-7388. https://doi.org/10.1002/int.22590
Thakkar, A., & Lohiya, R. (2023). Fusion of statistical importance for feature selection in Deep Neural Network-based Intrusion Detection System. Information Fusion, 90(1), 353-363. https://doi.org/10.1016/j.inffus.2022.09.026
Thirimanne, S. P., Jayawardana, L., Yasakethu, L., Liyanaarachchi, P., & Hewage, C. (2022). Deep neural network based real-time intrusion detection system. SN Computer Science, 3(2), Article 145. https://doi.org/10.1007/s42979-022-01031-1
Vishwakarma, M., & Kesswani, N. (2022). DIDS: A deep neural network based real-time Intrusion detection system for IoT. Decision Analytics Journal, 5(1), Article 100142. https://doi.org/10.1016/j.dajour.2022.100142
Wang, Z., Liu, Y., He, D., & Chan, S. (2021). Intrusion detection methods based on integrated deep learning model. Computers & Security, 10(3), Article 102177. https://doi.org/10.1016/j.cose.2021.102177