Securing Web Applications: A Machine Learning Approach for SQL Injection Threats

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 04 | 01 | Page :
    By

    Nilima D. Bobade,

  • Swati. S. Shereker,

  1. Assistant professor, Department of Computer Science, Sant Gadge Baba Amravati University, Maharashtra, India
  2. Assistant professor, Department of Computer Science, Sant Gadge Baba Amravati University, Maharashtra, India

Abstract

The rapid evolution and widespread adoption of the internet have significantly transformed the world, leading to an increased number of cyber-attacks. Cyber security has become one of the most critical challenges for society, incurring substantial financial losses annually. This research focuses on SQL Injection attacks the specific threat of on web applications, aiming to detect malicious queries designed to exploit vulnerabilities and access sensitive data. In recent years, the frequency of SQLi attacks has surged, posing severe risks to web application security. Attackers use SQLi to execute arbitrary SQL code, potentially gaining unauthorized access to databases, exfiltrating data, and compromising the integrity of web services. Despite various efforts to mitigate SQLi attacks, a comprehensive and effective detection mechanism remains elusive. This paper highlights the machine learning approach to identify and prevent SQLi attacks, providing a comparative analysis of various classifiers and their performance. The study underscores the potential of machine learning in enhancing web application security, offering adaptive and dynamic solutions to combat the evolving threat of SQLi attacks.

Keywords: SQL Injection, Threat Detection, Web Security, Machine Learning, Cyber Security

How to cite this article:
Nilima D. Bobade, Swati. S. Shereker. Securing Web Applications: A Machine Learning Approach for SQL Injection Threats. International Journal of Information Security Engineering. 2026; 04(01):-.
How to cite this URL:
Nilima D. Bobade, Swati. S. Shereker. Securing Web Applications: A Machine Learning Approach for SQL Injection Threats. International Journal of Information Security Engineering. 2026; 04(01):-. Available from: https://journals.stmjournals.com/ijise/article=2026/view=239123


References

  1. Brindavathi B, Karrothu A, Anilkumar C. An Analysis of AI-based SQL Injection (SQLi) Attack Detection. In2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) 2023 Aug 23 (pp. 31-35). IEEE.
  2. Hasan M, Balbahaith Z, Tarique M. Detection of SQL injection attacks: a machine learning approach. In2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA) 2019 Nov 19 (pp. 1-6). IEEE.
  3. Uwagbole SO, Buchanan WJ, Fan L. Applied machine learning predictive analytics to SQL injection attack detection and prevention. In2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) 2017 May 8 (pp. 1087-1090). IEEE.
  4. Valli Kumari V, Prasanna Kumar Y. SQL Injection Detection Using Recurrent Neural Networks (RNN). InInternational Conference on Security and Privacy 2024 Nov 20 (pp. 154-167). Cham: Springer Nature Switzerland.
  5. Gogoi B, Ahmed T, Dutta A. Defending against sql injection attacks in web applications using machine learning and natural language processing. In2021 IEEE 18th India Council International Conference (INDICON) 2021 Dec 19 (pp. 1-6). IEEE.
  6. Roy P, Kumar R, Rani P. SQL injection attack detection by machine learning classifier. In2022 International conference on applied artificial intelligence and computing (ICAAIC) 2022 May 9 (pp. 394-400). IEEE.
  7. Alkhathami JM, Alzahrani SM. Detection of SQL injection attacks using machine learning in cloud computing platform. J. Theor. Appl. Inf. Technol. 2022 Aug 15;100(15):1-4.
  8. Ashlam AA, Badii A, Stahl F. A novel approach exploiting machine learning to detect SQLi attacks. In2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET) 2022 Mar 22 (pp. 513-517). IEEE.
  9. Muliono Y, Darus MY, Pardomuan CR, Ariffin MA, Kurniawan A. Predicting Confidentiality, Integrity, and Availability from SQL Injection Payload. In2022 International Conference on Information Management and Technology (ICIMTech) 2022 Aug 11 (pp. 600-605). IEEE.
  10. Zhumabekova A, Matson ET, Karyukin V, Zhumabekova K, Zhuandykov B, Ussatova O, Telbayeva T. Determining Web Application Vulnerabilities Using Machine Learning Methods. In2023 19th International Asian School-Seminar on Optimization Problems of Complex Systems (OPCS) 2023 Aug 14 (pp. 136-139). IEEE.
  11. Zhang S, Li Y, Jiang Q. Feature ratio method: A payload feature extraction and detection approach for SQL injection attacks. In2023 3rd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) 2023 Feb 25 (pp. 172-175). IEEE.
Ahead of Print Subscription Original Research
Volume 04
01
Received 12/05/2025
Accepted 08/07/2025
Published 24/03/2026
Publication Time 316 Days
53

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