A Web-Based Automated Fuzzing Tool for Web Applications

References

[1] Kisten, M., Ezugwu, A. E. S., & Olusanya, M. O. (2024). Explainable artificial intelligence model for predictive maintenance in smart agricultural facilities. IEEE Access, 12, 24348–24367.

[2] Goel, N., Kaur, S., & Kumar, Y. (2022). Machine learning-based remote monitoring and predictive analytics system for crop and livestock. In AI, Edge and IoT-Based Smart Agriculture (pp. 395–407). Academic Press.

[3] Titirmare, S., Margal, P. B., Gupta, S., & Kumar, D. (2024). AI-powered predictive analytics for crop yield optimization. In Agriculture 4.0 (pp. 89–110). CRC Press.

[4] Kishor, I., Mamodiya, U., Patil, V., & Naik, N. (2025). AI-integrated autonomous robotics for solar panel cleaning and predictive maintenance using drone and ground-based systems. Scientific Reports, 15(1), 32187.

[5] Shuriya, B. (2026). Adoption of Deep Learning Driven Precision Agriculture for Optimizing Crop Productivity and Soil Health via Predictive Analytics and Autonomous Sensing Mechanisms.

[6] Kar, P., & Chowdhury, S. (2024). IoT and drone-based field monitoring and surveillance system. In Artificial Intelligence Techniques in Smart Agriculture (pp. 253–266). Singapore: Springer Nature Singapore.

[7] Geetha, K., & Deepika, J. (2024). AI-driven drone-assisted smart farming framework for precision pest control and crop yield optimization. National Journal of Smart Agriculture and Rural Innovation, 11–19.

[8] Hernández Hernández, G. C., Gómez Gómez, J., & Jiménez-Cabas, J. (2025). Predictive models based on artificial intelligence to estimate crop yield: A literature review. Agriculture, 15(23), 2438.

[9] Liang, M., Nan, L., Peng, B., & Bo, G. (2026). Economic returns from AI-driven precision agriculture in degraded ecosystems: Productivity effects measured using UAV remote sensing. Land Degradation & Development.

[10] Guatno, C. P. V., Obillo, D. E. A., Taguinod, J. E., Sison, C. A. A. R. C., Dioses, R. M., & Abando, D. S. (2024, December). Harnessing AI for agriculture utilizing color-condition camera sensors and thermal imaging drones for crop color-condition detection and predictive yield analysis with inventory management system. In International Conference on Green Energy, Computing and Intelligent Technology 2024 (GEn-CITy 2024) (Vol. 2024, pp. 345–352). IET.

[11] Singh, D. P., Reddy, P. C. P., Devayani, G., Poongothai, S., Suganthi, G., & Babu, G. C. (2025, August). Drone-assisted precision agriculture with hybrid machine learning models for sustainable farming. In 2025 Global Conference on Information Technology and Communication Networks (GITCON) (pp. 1–6). IEEE.

[12] Ramteke, S. V., Varadwaj, P. K., & Tiwari, V. (2025, December). AI-enabled life cycle assessment of UAV-based spraying systems for climate-smart agriculture and SDG monitoring. In 2025 IEEE 17th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 1708–1712). IEEE.

[13] Borah, S. K., Pal, D., Sarkar, S., & Sethi, L. N. (2025). AI-powered drones for sustainable agriculture and precision farming. In Advancing Global Food Security with Agriculture 4.0 and 5.0 (pp. 69–98). IGI Global Scientific Publishing.

[14] Arsenoaia, V. N., Topa, D. C., Ratu, R. N., & Tenu, I. (2026). From sensing to intervention: A critical review of agricultural drones for precision agriculture, data-driven decision making, and sustainable intensification. Agronomy, 16(5), 564.

[15] Renuka, A. (2025). AI-driven predictive analytics in precision agriculture. Scientific Journal of Artificial Intelligence and Blockchain Technologies, 2(3), 9–17.

[16] Kumar, P., & Choudhury, D. (2025). Advancements in precision agriculture: Integrating machine learning techniques for crop monitoring and management. In Artificial Intelligence in Microbial Research: Bridging the Gap (pp. 29–57). Singapore: Springer Nature Singapore.

[17] Ugwu, O. P. C., Ogenyi, F. C., Alum, E. U., Eze, V. H. U., Basajja, M., Ugwu, J. N., Ugwu, C. N., Ejemot-Nwadiaro, R. I., Okon, M. B., Egba, S. I., & Ejim, U. D. (2025). Implementing artificial intelligence and machine learning algorithms for optimized crop management: A systematic review on data-driven approach to enhancing resource use and agricultural sustainability. Cogent Food & Agriculture, 11(1), 2569982.

[18] Ali, Z., Muhammad, A., Lee, N., Waqar, M., & Lee, S. W. (2025). Artificial intelligence for sustainable agriculture: A comprehensive review of AI-driven technologies in crop production. Sustainability, 17(5), 2281.

[19] Mohyuddin, G., Khan, M. A., Haseeb, A., Mahpara, S., Waseem, M., & Saleh, A. M. (2024). Evaluation of machine learning approaches for precision farming in smart agriculture systems: A comprehensive review. IEEE Access, 12, 60155–60184.

[20] Melki, M. N. E., Faqeih, K. Y., Alamri, S., AlAmri, A. R., Aldubehi, M. A., & Alamery, E. R. (2025). Integrating artificial intelligence, drones, robotics, and sensors for sustainable and climate-resilient agriculture: A critical review. Sustainability, 2025, 1–20.

[21] Pramanik, S., Roy, S., & Bose, R. (Eds.). (2024). Data Driven Mathematical Modeling in Agriculture: Tools and Technologies. CRC Press.

[22] Melesse, T. Y. (2025). Digital twin-based applications in crop monitoring. Heliyon, 11(2).

[23] Mundappat Ramachandran, M., Fahad Mon, B., Hayajneh, M., Abu Ali, N., & Badidi, E. (2025). Solar Agro Savior: Smart agricultural monitoring using drones and deep learning techniques. Agriculture, 15(15), 1656.

[24] Seethapathy, P., Kannan, M., & Manalil, S. (2026). AI-enabled early anomaly detection of crop diseases at real-field environments. In Harnessing AI to Reshape the Future of Agriculture (pp. 281–308). Cham: Springer Nature Switzerland.

[25] Hassan, M. (2025). AI-Based Conditional Monitoring and Predictive Maintenance for Offshore Wind Farms.

[26] Almannaei, K. J. (2024). Predictive Maintenance on Drone Batteries Failure Using Machine Learning (Master’s thesis). Rochester Institute of Technology.

[27] Elufioye, O. A., Ike, C. U., Odeyemi, O., Usman, F. O., & Mhlongo, N. Z. (2024). AI-driven predictive analytics in agricultural supply chains: A review assessing the benefits and challenges of AI in forecasting demand and optimizing supply in agriculture. Computer Science & IT Research Journal, 5(2), 473–497.

[28] Singh, P., Singh, M. K., Singh, N., & Chakraverti, A. (2023). IoT and AI-based intelligent agriculture framework for crop prediction. International Journal of Sensors, Wireless Communications and Control, 13(3), 145–154.

[29] Elbasi, E., Alzoubi, Y. I., Topcu, A. E., & Nadeem, M. (2025). Green AI for smart agriculture: Energy-efficient predictive models for crop yield and resource management. IEEE Access, 13, 204924–204953.

[30] Sudha, S. P., & Loret, J. B. (2026). A review on machine learning-based precision agriculture techniques for crop farming monitoring with IoT. Discover Environment, 4(1), 10.

@article{1718982,
      author = {Ritik Dhankhar, Ajay Sharma, Shakti Arora},
      title = {A Web-Based Automated Fuzzing Tool for Web Applications},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {1},
      pages = {2235-2243},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718982.pdf},
      abstract = {The importance of Web Application Security grows daily as more organizations are threatened and attacked by cyber criminals. With the growing threat from cyber criminals, performing security testing to identify vulnerabilities in web systems is critical. Of all security testing techniques, fuzz testing is perhaps the best technique available today. Fuzz testing involves injecting input into a target application, including mal- formed, unexpected, or random data to see how it reacts when it receives bad data. In the case of web applications, fuzz testing is done by sending numerous HTTP requests (each request contains different forms of crafted or invalid data) to a web server to measure the response generated by the server. This study will create an Automated Web Application Fuzzer which will be integrated with Jenkins so that continuous security testing of Web Applications can occur. Test cases were created using known security vulnerabilities within web applications. Testing revealed that the automation tool found vulnerabilities in thirteen (13) out of fifteen (15) test cases. Therefore, testing reveals that the majority of web vulnerabilities can be easily identified simply by reviewing the content of HTTP responses, thereby validating the effectiveness of the proposed auto- mated web application fuzzing methodology},
      keywords = {Web Application Security, Fuzz Testing, Automated Vulnerability Detection, HTTP Request Testing},
      month = {July},
      doi = {https://doi.org/10.64388/IREV9I1-1718982}
  }