Abstract
Web application firewalls (WAFs) are frequently utilized since they are simple services and offer considerable defense against various cyber attacks. However, based on rules and signatures, traditional WAFs have significant false positive rates (34%.
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Notes
- 1.
Available at https://www.isi.csic.es/dataset/.
- 2.
Available at https://www.kaggle.com/datasets/antonyj453/urldataset.
- 3.
- 4.
Available at https://github.com/yoeo/guesslang.
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Acknowledgment
We acknowledge Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, for supporting this study.
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Trinh, CV., Le, TT., Le-Nguyen, MK., Le, DT., Nguyen, VH., Nguyen-An, K. (2023). An Efficient Machine Learning-Based Web Application Firewall with Deep Automated Pattern Categorization. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2023. Communications in Computer and Information Science, vol 1925. Springer, Singapore. https://doi.org/10.1007/978-981-99-8296-7_15
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