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SQL Injection Detection using Machine Learning: A Review

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dc.contributor.authorMohammed A M Oudahen_US
dc.contributor.authorMohd Fadzli Marhusinen_US
dc.date.accessioned2024-05-30T02:07:11Z
dc.date.available2024-05-30T02:07:11Z
dc.date.issued2024
dc.descriptionMalaysian Journal of Science, Health & Technology (MJoSHT) Volume 10 Issue 1 Page (50-57)en_US
dc.description.abstractSQL injection attacks are critical security vulnerability exploitation in web applications, posing risks to data, if successfully executed, allowing attackers to gain unauthorised access to sensitive data. Due to the absence of a standardised structure, traditional signature-based detection methods face challenges in effectively detecting SQL injection attacks. To overcome this challenge, machine learning(ML)algorithms have emerged as a promising approach for detecting SQL injection attacks. This paper presents a comprehensive literature review on the utilisation of MLtechniques for SQL injection detection. The review covers various aspects, including dataset collection, feature extraction, training, and testing, with different MLalgorithms. The studies included in the review demonstrate high levels of accuracy in detecting attacks and reducing false positives.en_US
dc.identifier.citationMohammed A M Oudah, & Mohd Fadzli Marhusin. (2024). SQL Injection Detection using Machine Learning: A Review. Malaysian Journal of Science Health & Technology, 10(1), 39–49. https://doi.org/10.33102/mjosht.v10i1.368en_US
dc.identifier.doi10.33102/mjosht.v10i1.368
dc.identifier.epage57
dc.identifier.issn2601-0003
dc.identifier.issue1
dc.identifier.spage50
dc.identifier.urihttps://mjosht.usim.edu.my/index.php/mjosht/article/view/368/220
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/15405
dc.identifier.volume10
dc.language.isoen_USen_US
dc.publisherUSIM PRESSen_US
dc.relation.ispartofMalaysian Journal of Science, Health & Technology (MJoSHT)en_US
dc.subjectCybersecurity; Machine Learning; SQL Injection Detectionen_US
dc.titleSQL Injection Detection using Machine Learning: A Reviewen_US
dc.typeArticleen_US
dspace.entity.typePublication

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