Integrating Fuzzy Logic and Deep Learning for Effective Network Attack Detection with Fuzzified Deep Convolutional Neural Network
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-54696-9_4
Reference17 articles.
1. Das, R., Sen, S., & Maulik, U. (2020). A survey on fuzzy deep neural networks. ACM Computing Surveys (CSUR), 53(3), 1–25.
2. Liu, M., Zhou, Z., Shang, P., & Xu, D. (2019). Fuzzified image enhancement for deep learning in iris recognition. IEEE Transactions on Fuzzy Systems, 28(1), 92–99.
3. GSR, E. S., Azees, M., Vinodkumar, C. R., & Parthasarathy, G. (2022). Hybrid optimization enabled deep learning technique for multi-level intrusion detection. Advances in Engineering Software, 173, 103197.
4. Javaheri, D., Gorgin, S., Lee, J. A., & Masdari, M. (2023). Fuzzy logic-based DDoS attacks and network traffic anomaly detection methods: Classification, overview, and future perspectives. Information Sciences.
5. Mishra, P., Varadharajan, V., Tupakula, U., & Pilli, E. S. (2018). A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE communications surveys & tutorials, 21(1), 686–728.
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