Improving detection of SQL injection attack by SMOTE

Name: Poramin Sri-thong

Organization : Chiang Mai University. Faculty of Science

Email : poramin_sr@cmu.ac.th

Name: Chumphol Bunkhumpornpat

Organization : Chiang Mai University. Faculty of Science

Email : chumphol.b@cmu.ac.th

keyword: SQL injection

LCSH: Machine learning

; Rul-based detection

; SMOTE

Abstract: This paper proposes competitive efficiency study and methods for detecting SQL injection by using rule base and machine learning. Rule-base employs the black-list word technique while machine learning includes Naive Bayes, random forest, and the K-Nearest Neighbor. We also apply SMOTE to improve the prediction rate on a minority class (injection class). As a result, machine learning algorithms that use the random forest and KNN efficiently detect very similar patterns as good as rule-based detection

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2023

Modified: 2024-04-23

Issued: 2024-04-23

บทความ/Article

application/pdf

BibliograpyCitation : In Institute of Electrical and Electronics Engineers. The 27th International Computer Science and Engineering Conference 2023 (ICSEC 2023) (pp.243-246). Bangkok : Institute of Electrical and Electronics Engineers

eng

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