Advances in Engineering Innovation

Advances in Engineering Innovation

Vol. 2, 07 October 2023


Open Access | Article

SQL injection attacks

Jene Wrightes * 1
1 Embry Riddle University

* Author to whom correspondence should be addressed.

Advances in Engineering Innovation, Vol. 2, 25-30
Published 07 October 2023. © 07 October 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Jene Wrightes. SQL injection attacks. AEI (2023) Vol. 2: 25-30. DOI: 10.54254/2977-3903/2/2023017.

Abstract

SQL Injection (SQLi) attacks continue to pose significant threats to modern web applications, compromising data integrity and confidentiality. This research delves into the development and evaluation of methodologies designed to detect and mitigate these malicious attacks. Employing a diverse set of web applications, the study unfolds in a controlled environment, simulating real-world conditions to assess the effectiveness of current defense mechanisms against SQLi. Building upon this baseline, the research introduces a two-pronged defense mechanism: a Static Analysis Tool to pre-emptively identify vulnerabilities in application code and a Runtime Query Sanitizer that employs rule-based patterns and machine learning models to scrutinize and sanitize SQL queries in real-time. Performance evaluation metrics, encompassing detection rate, false positives, response time, and machine learning efficiency, are meticulously documented. Further robustness of these mechanisms is ascertained through real-world simulations involving unsuspecting users and ethical hackers. Initial results indicate promising potential for the introduced methodologies in safeguarding web applications against SQLi attacks. The study's findings serve as a critical step towards fortifying web applications, emphasizing the amalgamation of static analysis and real-time query sanitization as an effective countermeasure against SQLi threats.

Keywords

SQL Injection (SQLi), static analysis, runtime query sanitization, web application security, machine learning models

References

1. Anley, C. (2002). Advanced SQL injection in SQL Server applications. Next Generation Security Software Ltd.

2. Boyd, S. W., & Keromytis, A. D. (2004). SQLrand: Preventing SQL injection attacks. In Proceedings of the 2nd Applied Cryptography and Network Security (ACNS) Conference.

3. Halfond, W. G., Viegas, J., & Orso, A. (2006). A classification of SQL-injection attacks and countermeasures. In Proceedings of the IEEE International Symposium on Secure Software Engineering, 1(1), 13-15.

4. OWASP. (2021). OWASP Top Ten. Open Web Application Security Project. [URL]

5. Halfond, W. G., Viegas, J., & Orso, A. (2006). A classification of SQL-injection attacks and countermeasures. In Proceedings of the IEEE International Symposium on Secure Software Engineering.

6. Wagner, R., & Dean, D. (2007). Intrusion Detection via Static Analysis. IEEE Symposium on Security and Privacy.

7. Spett, K. (2009). Detecting SQL Injection Vulnerabilities in Web Services. International Journal of Web Application Security, 3(2), 123-137.

8. Russo, A., & Smith, J. (2008). Advancements in SQLi Attack Patterns and Defense Mechanisms. ACM Transactions on Web Security, 4(1), 12-28.

9. Chen, L., & Williams, D. (2010). Machine Learning for SQL Injection Prevention. Conference on Web Security Research.

10. Barnes, M., & Park, J. (2011). Real-time Monitoring and Defense against SQL Injection. IEEE Transactions on Dependable and Secure Computing, 8(3), 466-479.

11. Thompson, A., & Chase, C. (2012). Runtime Analysis of Web Applications for SQLi Detection. Proceedings of the International Workshop on Web Application Security.

12. Gupta, S., & Gupta, B. (2013). A Comparative Analysis of SQLi Defense Mechanisms. Journal of Computer Security, 21(4), 545-568.

13. Lee, H., & Kim, J. (2014). Database Firewalls: An Application-Centric Approach to Preventing SQL Injection. International Conference on Cybersecurity and Cloud Computing.

14. Wright, R., & Patel, V. (2015). Static vs. Dynamic Analysis in Detecting SQLi Vulnerabilities. ACM Symposium on Web Application Security.

15. Anderson, L., & Foster, J. (2016). Towards a Safer Web: Techniques and Tools for Preventing SQL Injection. International Journal of Network Security, 18(1), 1-15.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
ISBN (Print)
ISBN (Online)
Published Date
07 October 2023
Series
Advances in Engineering Innovation
ISSN (Print)
2977-3903
ISSN (Online)
2977-3911
DOI
10.54254/2977-3903/2/2023017
Copyright
07 October 2023
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated