An Efficient Machine Learning-Based Web Application Firewall with Deep Automated Pattern Categorization

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

Available at https://www.isi.csic.es/dataset/. Available at https://www.kaggle.com/datasets/antonyj453/urldataset. Available at https://www.kaggle.com/datasets/simiotic/github-code-snippets. 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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  1. Ho Chi Minh city University of Technology (HCMUT), VNU-HCM, Ho Chi Minh City, Vietnam Cong-Vu Trinh, Thien-Thanh Le, Minh-Khoi Le-Nguyen, Dinh-Thuan Le & Khuong Nguyen-An
  2. Polaris Infosec Pte. Ltd., Ho Chi Minh City, Vietnam Van-Hoa Nguyen
  1. Cong-Vu Trinh
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  1. Ho Chi Minh City University of Industry and Trade, Ho Chi Minh City, Vietnam Tran Khanh Dang
  2. Johannes Kepler University of Linz, Linz, Austria Josef Küng
  3. Sungkyunkwan University, Suwon-si, Korea (Republic of) Tai M. Chung

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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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