Optimizing SQL injection detection using BERT encoding and AdaBoost Classification

DOI

10.2991/978-94-6463-482-2_10How to use a DOI?

Keywords

SQL injection; BERT; AdaBoost; Metaheuristics optimization; Swarm intelligence; WOA

Abstract

SQL injection attacks are still considerable threat to the web applications and organizations security in general, giving the attackers the opportunity to cause execution of arbitrary SQL queries sent through user input fields. Traditional defensive mechanisms to mitigate these threats often rely on static rules that may not adapt efficiently to the ever-evolving attack patterns. Recently, machine learning models are regarded as very promising to detect and prevent these attacks by enhancing the strenght of data-driven methods. This research proposes AdaBoost classifier to mitigate SQL threats. An altered variant of whale optimization algorithm has been introduced and employed to optimize the hyperparameters of the AdaBoost for this challenging problem. The outcomes were compared to the scores attained by other powerful optimizers. The suggested method achieved supreme results, with the highest obtained accuracy of slightly over 98.9%, exhibiting exciting potential in this field.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Miodrag Zivkovic
AU  - Luka Jovanovic
AU  - Milos Bukumira
AU  - Milos Antonijevic
AU  - Djordje Mladenovic
AU  - Maryam Al Washahi
AU  - Nebojsa Bacanin
PY  - 2024
DA  - 2024/08/23
TI  - Optimizing SQL injection detection using BERT encoding and AdaBoost Classification
BT  - Proceedings of the 2nd International Conference on Innovation in Information Technology and Business (ICIITB 2024)
PB  - Atlantis Press
SP  - 137
EP  - 154
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-482-2_10
DO  - 10.2991/978-94-6463-482-2_10
ID  - Zivkovic2024
ER  -