Optimizing Cloudflare security and performance with AI-based Web Application Firewall and anomaly detection
References
- M. Krishnan, Y. Lim, S. Perumal, and G. Palanisamy, “Detection and defending the XSS attack using novel hybrid stacking ensemble learning-based DNN approach,” Digit. Commun. Netw., vol. 2352–8648, 2022.
- A. Moradi Vartouni, M. Teshnehlab, and S. Sedighian Kashi, “Leveraging deep neural networks for anomaly-based web application firewall,” IET Inf. Secur., vol. 13, pp. 352–361, 2019.
- S. Hao, J. Long, and Y. Yang, “Bl-ids: Detecting web attacks using bi-LSTM model based on deep learning,” in Proc. 2nd EAI Int. Conf. Security and Privacy in New Computing Environments (SPNCE), Tianjin, China, Apr. 2019, pp. 551–563.
- P. Jakić, F. Hajjaj, J. Ibrahim, and A. Elsadai, “The overview of intrusion detection system methods and techniques,” in Proc. Sinteza 2019 - Int. Sci. Conf. Inf. Technol. Data Related Res., Belgrade, Serbia, 2019, pp. 155–161.
- A. Moradi Vartouni, S. Mehralian, M. Teshnehlab, and S. Sedighian Kashi, “Auto-Encoder LSTM methods for anomaly-based web application firewall,” Int. J. Inf. Commun. Technol. Res., vol. 11, pp. 49–56, 2019.
- Z. Tian, C. Luo, J. Qiu, X. Du, and M. Guizani, “A distributed deep learning system for web attack detection on edge devices,” IEEE Trans. Ind. Inform., vol. 16, pp. 1963–1971, 2019.
- K. O. A. Alimi, K. Ouahada, A. M. Abu-Mahfouz, S. Rimer, and O. A. Alimi, “Refined LSTM based intrusion detection for denial-of-service attack in Internet of Things,” J. Sens. Actuator Netw., vol. 11, no. 32, 2022.
- Y. E. Seyyar, A. G. Yavuz, and H. M. Ünver, “An attack detection framework based on BERT and deep learning,” IEEE Access, vol. 10, pp. 68633–68644, 2022.
- E. Pantoulas, “Description, analysis and implementation of a web application firewall (WAF). Creation of attack scenarios and threats prevention,” M.S. thesis, Sch. Inf. Technol. Commun., Piraeus, Greece, 2022.
- R. L. Alaoui and E. H. Nfaoui, “Deep learning for vulnerability and attack detection on web applications: A systematic literature review,” Future Internet, vol. 14, no. 118, 2022.
- S. Widup, M. Spitler, D. Hylender, and G. Bassett, “Verizon Data Breach Investigations Report,” Tech. Rep., 2018. [Online]. Available:
https://www22.verizon.com/wholesale/contenthub/data_breach_investigation_report.html [Accessed: Jan. 15, 2023]. - M. Rusyaidi, S. Jaf, and Z. Ibrahim, “Detecting distributed denial of service in network traffic with deep learning,” Int. J. Adv. Comput. Sci. Appl., vol. 13, pp. 34–41, 2022.
- H. Fujita and H. Perez-Meana, “LSTM neural networks for detecting anomalies caused by web application cyber attacks,” in New Trends in Intelligent Software Methodologies, Tools and Techniques, vol. 337, IOS Press, Amsterdam, Netherlands, 2021, p. 127.
- N. Montes, G. Betarte, R. Martínez, and A. Pardo, “Web application attacks detection using deep learning,” in Proc. Iberoamerican Congress on Pattern Recognition, Porto, Portugal, May 2021, pp. 227–236.
- Y. Pan, F. Sun, Z. Teng, J. White, D. C. Schmidt, J. Staples, and L. Krause, “Detecting web attacks with end-to-end deep learning,” J. Internet Serv. Appl., vol. 10, pp. 1–22, 2019.
- S. Rajesh, M. Clement, S. B. S, A. S. S. H., and J. Johnson, “Real-time DDoS attack detection based on machine learning algorithms,” in Proc. Yukthi 2021 - Int. Conf. Emerging Trends in Eng., GEC Kozhikode, Kerala, India, Sep. 2021.
- C. Lente, R. Hirata Jr., and D. M. Batista, “An improved tool for detection of XSS attacks by combining CNN with LSTM,” in Proc. XXI Symp. Brasileiro em Segurança da Informação e de Sistemas Computacionais, Florianópolis, Brazil, Sep. 2021, pp. 1–8.
- H. Karacan and M. Sevri, “A novel data augmentation technique and deep learning model for web application security,” IEEE Access, vol. 9, pp. 150781–150797, 2021.
- A. Tekerek, “A novel architecture for web-based attack detection using convolutional neural network,” Comput. Secur., vol. 100, p. 102096, 2021.
- V. N. Anand and K. Chitra, “Anomaly detection system using machine learning algorithms,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 30, Nov. 2024.
- E. Leka, L. Lamani, and A. Aliti, “Web application firewall for detecting and mitigation of based DDoS attacks using machine learning and blockchain,” TEM J., vol. 27, Nov. 2024.
- M. I. Schol, “Web application firewall using machine learning and features engineering,” Security and Communication Networks, Jun. 2022.
- S. Ness, V. Eswarakrishnan, and H. Sridharan, “Anomaly detection in network traffic using advanced machine learning techniques,” IEEE Access, vol. 1, Jan. 2025.
- Z. Cheng, B. Cui, and T. Qi, “An improved feature extraction approach for web anomaly detection based on semantic structure,” Security Commun. Netw., vol. 11, Feb. 2021.
- L. Benova and L. Hudec, “Web server load prediction and anomaly detection from hypertext transfer protocol logs,” Int. J. Power Electron. Drive Syst., vol. 1, Oct. 2023.
- C. D. Xuan, N. T. Nguyen, and H. N. Dinh, “An adaptive anomaly request detection framework based on dynamic web application profiles,” Int. J. Electr. Comput. Eng., vol. 1, Oct. 2020.
- “A firewall policy anomaly detection framework for reliable network security,” IEEE Trans. Reliab., vol. 1, Mar. 2022.
- T. Alma and M. L. Das, “Web application attack detection using deep learning,” arXiv: Cryptography and Security, Nov. 2020.
- “Anomaly detection system and resolution of anomalies for firewall policies,” Algorithms Intell. Syst., vol. 1, Jan. 2022.
- P. R. Kannari, N. S. Chowdary, and R. L. Biradar, “An anomaly-based intrusion detection system using recursive feature elimination technique for improved attack detection,” Theor. Comput. Sci., vol. 1, Jul. 2022.
- N. Nyakuchena and C. Tsikada, “Enhancing supply chain resilience through artificial intelligence and machine learning,” Advances in Marketing, Customer Relationship Management, and E-Services Book Series, 18 Oct. 2024.
- R. U. Attah, B. M. P. Garba, I. Gil-Ozoudeh, and O. Iwuanyanwu, “Enhancing supply chain resilience through artificial intelligence: Analyzing problem-solving approaches in logistics management,” International Journal of Management & Entrepreneurship Research, 4 Dec. 2024.
- I. Alsakhen, L. Buics, and E. Süle, “AI-driven resilience in revolutionizing supply chain management: A systematic literature review,” Journal of Infrastructure, Policy and Development, 24 Dec. 2024.
- D. Zhang, “AI integration in supply chain and operations management: Enhancing efficiency and resilience,” Applied and Computational Engineering, 27 Aug. 2024.
- S. F. Wamba and M. M. Queiroz, “A framework based on blockchain, artificial intelligence, and big data analytics to leverage supply chain resilience considering COVID-19,” Advances in Control and Optimization of Dynamical Systems, 26 Oct. 2022.
- A. O. Yekeen, C. P.-M. Ewim, and N. J. Sam-Bulya, “Enhancing supply chain security and transparency with AI and blockchain integration for future-proof solutions,” International Journal of Frontline Research in Science and Technology, 19 Oct. 2024.
- Y. Zhou, “Blockchain technology improves the resilience of manufacturing supply chain: An exploration based on grounded theory,” Advances in Economics, Management and Political Sciences, 19 Dec. 2024.
- F. C. Okonkwo, B. G. Akonor, and T. K. Adukpo, “Artificial intelligence in healthcare supply chain management: Enhancing resilience and efficiency in U.S. medical supply distribution,” EPRA International Journal of Economics, Business and Management, 27 Jan. 2025.
- Z.-Y. Yang, X. Guo, J. Sun, Y. Zhang, and Y. Wang, “What does not kill you makes you stronger: Supply chain resilience and corporate sustainability through emerging IT capability,” IEEE Transactions on Engineering Management, 1 Jan. 2022.
- A. Samuels, “Examining the integration of artificial intelligence in supply chain management from Industry 4.0 to 6.0: A systematic literature review,” Frontiers in Artificial Intelligence, 20 Jan. 2025.
- H. E. Adama, O. A. Popoola, C. D. Okeke, and A. E. Akinoso, “Economic theory and practical impacts of digital transformation in supply chain optimization,” International Journal of Advanced Economics, 26 Apr. 2024.
- J. Rana and Y. Daultani, “Mapping the role and impact of artificial intelligence and machine learning applications in supply chain digital transformation: A bibliometric analysis,” Operations Management Research, 28 Dec. 2022.
- B. Q. Khoa, H. Q. Nguyen, D. B. H. Anh, and N. M. Ngọc, “Impact of artificial intelligence’s part in supply chain planning and decision-making optimization,” International Journal of Multidisciplinary Research and Growth Evaluation, 1 Jan. 2024.
- A. S. Kassa, D. Kitaw, U. Stache, B. Beshah, and G. Degefu, “Artificial intelligence techniques for enhancing supply chain resilience: A systematic literature review, holistic framework, and future research,” Computers & Industrial Engineering, 1 Dec. 2023.
- D. Aggarwal, D. Sharma, and A. B. Saxena, “Role of AI in cyber security through anomaly detection and predictive analysis,” J. Informat. Educ. Res., vol. 3, no. 2, pp. 1–12, 2023.
DOI: https://doi.org/10.2478/ijssis-2025-0040 | Journal eISSN: 1178-5608
Language: English
Submitted on: Jan 22, 2025
Published on: Aug 8, 2025
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Keywords:
Related subjects:
© 2025 Kusumakumari Daram, P. Senthilkumar, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.