PIPELINE TIỀN XỬ LÝ DỮ LIỆU MẠNG CHO MÔ HÌNH CNN PHÁT HIỆN XÂM NHẬP ĐA LỚP
Nội dung chính của bài viết
Tóm tắt
Việc ứng dụng công nghệ học sâu để xây dựng các mô hình phát hiện tấn công mạng đang trở thành một xu hướng phổ biến. Trong đó, cách tiếp cận trực quan hóa dữ liệu mạng nhằm tạo ra các biểu diễn ảnh quen thuộc với mạng nơ-ron tích chập (Convolutional Neural Network – CNN), tương tự như trong lĩnh vực thị giác máy tính, được xem là một hướng nghiên cứu sáng tạo và tiềm năng. Tuy nhiên, khả năng phát hiện đồng thời nhiều loại tấn công mạng bằng các mô hình CNN đa tầng trên các bộ dữ liệu mạng đã được chuyển đổi sang dạng ảnh vẫn còn nhiều hạn chế. Trong bài báo này, chúng tôi đề xuất một phương pháp tiền xử lý dữ liệu mạng trước khi chuyển đổi sang biểu diễn ảnh để làm đầu vào cho các mô hình học sâu dựa trên CNN. Phương pháp được áp dụng và đánh giá trên bộ dữ liệu NSL-KDD với tập kiểm tra KDDTest+ tách biệt, cho thấy hiệu quả đáng khích lệ. Kết quả thực nghiệm cũng khẳng định rằng tiền xử lý dữ liệu vẫn là một bước thiết yếu khi áp dụng học sâu trong việc xây dựng các mô hình phát hiện tấn công mạng.
Từ khóa
Tiền xử lý dữ liệu, NSL-KDD, CNN
Chi tiết bài viết
Tài liệu tham khảo
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Dhanabal, L., & Shantharajah, S. (2015). A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. International Journal of Advanced Research in Computer and Communication Engineering, 4(6), 446–452.
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Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350–361.
Liu, H., & Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. Applied Sciences, 9(20), 4396.
Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019). Deep learning approach for intelligent intrusion detection system. IEEE Access, 7, 41525–41550.
Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). A detailed analysis of the KDD Cup 99 data set. In Proceedings of the IEEE Symposium on Computational Intelligence for Security and Defense Applications (pp. 1–6). IEEE.
Thaseen, I. S., & Kumar, C. A. (2017). Intrusion detection model using fusion of chi-square feature selection and multiclass SVM. Journal of King Saud University – Computer and Information Sciences, 29(4), 462–472.
Tama, B. A., Comuzzi, M., & Rhee, K.-H. (2019). TSE-IDS: A two-stage classifier ensemble for intelligent anomaly-based intrusion detection system. IEEE Access, 7, 94497–94507.
Aldweesh, A., Derhab, A., & Emam, A. Z. (2019). Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues. Knowledge-Based Systems, 189, 105124.
Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2019). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications, 50, 102419.
Altwaijry, N., Alqahtani, A., & Al-Turaiki, I. (2019). A deep learning approach for anomaly-based network intrusion detection. In Y. Tian, T. Ma, & M. K. Khan (Eds.), Proceedings of the First International Conference on Big Data and Security. Springer.
Masum, M., Shahriar, H., & Haddad, H. M. (2021). A transfer learning with deep neural network approach for network intrusion detection. International Journal of Intelligent Computing Research, 12(1), 1087–1095.
He, H., Bai, Y., Garcia, E. A., & Li, S. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In Proceedings of the IEEE International Joint Conference on Neural Networks (pp. 1322–1328). IEEE.
Potdar, K. (2017). A comparative study of categorical variable encoding techniques for neural network classifiers. International Journal of Computer Applications, 175(4).
Al-Turaiki, I., & Altwaijry, N. (2021). A convolutional neural network for improved anomaly-based network intrusion detection. Cybersecurity, 4(3).
Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access, 5, 21954–21961.
Al-Qatf, M., Lasheng, Y., Al-Habib, M., & Al-Sabahi, K. (2018). Deep learning approach combining sparse autoencoder with SVM for network intrusion detection. IEEE Access, 6, 52843–52856.
NSL-KDD Dataset. (n.d.). NSL-KDD intrusion detection dataset. https://www.unb.ca/cic/datasets/nsl.html
Sherin, V. J. I. J., & Radhika, N. (2022). Stacked ensemble IDS using NSL-KDD dataset. Journal of Pharmaceutical Negative Results, 13(3).
Dhanabal, L., & Shantharajah, S. (2015). A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. International Journal of Advanced Research in Computer and Communication Engineering, 4(6), 446–452.
Dung, H. T., & Nguyen, H. S. (2023). A data preprocessing method prior to imaging for training deep learning-based network intrusion detection models. In Proceedings of the 26th National Conference on Electronics, Communications and Information Technology (REV-ECIT 2023) (pp. 311–316). Information and Communications Publishing House.
Jang, J., An, Y., Kim, D., & Choi, D. (2023). Feature importance-based backdoor attack in NSL-KDD. Electronics, 12(24), 4953. https://doi.org/10.3390/electronics12244953
Iftikhar, N., Rehman, M. U., Shah, M. A., Alenazi, M. J. F., & Ali, J. (2025). Intrusion detection in NSL-KDD dataset using hybrid self-organizing map model. Computer Modeling in Engineering & Sciences, 143(1), 639–671.
Wu, Y., & Hu, X. (2022). An intrusion detection method based on fully connected recurrent neural network. Scientific Programming, 2022, 1–11. https://doi.org/10.1155/2022/7777211
Wisanwanichthan, T., & Thammawichai, M. (2025). A lightweight intrusion detection system for IoT and UAV using deep neural networks with knowledge distillation. Computers, 14(7). https://doi.org/10.3390/computers14070291