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All Journal International Journal of Informatics, Information System and Computer Engineering (INJIISCOM)
Salih Abdullah, Hilmi
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Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Detection of SQL Injection Attacks Based on Supervised Machine Learning Algorithms: A Review Salih Abdullah, Hilmi; Mohsin Abdulazeez, Adnan
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 2 (2024): INJIISCOM: VOLUME 5, ISSUE 2, DECEMBER 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i2.12731

Abstract

In the ever-changing world of cybersecurity, it is becoming more important to ensure integrity of web applications as well as securing sensitive data. Among a variety of vulnerabilities, SQL injection is considered a significant risk with severe consequences. Addressing this crucial threat has always attracted the researchers to explore various approaches to identify and detect SQL injection attacks. The machine learning has captured the attention of the researchers to explore its potential due to its success in several different fields and the limitation of other rule-based approaches. This study provides a comprehensive review on a variety of the most recent researches that have been carried out using supervised learning algorithms. The study reveals that machine learning has a huge potential in the process of identification and detection of SQL injection attacks.
Co-Authors Mohsin Abdulazeez, Adnan