Structured query language injection detection with natural language processing techniques optimized by metaheuristics

Aleksandar Jokic; Nikola Jovic; Vuk Gajic; Marina Svicevic; Milos Pavkovic; Aleksandar Petrovic

doi:10.2991/978-94-6463-482-2_11

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Structured query language injection detection with natural language processing techniques optimized by metaheuristics

Authors

Aleksandar Jokic¹, Nikola Jovic¹, Vuk Gajic¹, Marina Svicevic², Milos Pavkovic¹, Aleksandar Petrovic¹^{, *}

¹Singidunum University, Faculty of Informatics and Computing, Danijelova 32, 11000, Belgrade, Serbia

²University of Kragujevac, Faculty of Science, Radoja Domanovića 12, 34000, Kragujevac, Serbia

^*Corresponding author. Email: aleksandar.petrovic@singdinum.ac.rs

Corresponding Author

Aleksandar Petrovic

Available Online 23 August 2024.

DOI: 10.2991/978-94-6463-482-2_11 How to use a DOI?
Keywords: sql injection; swarm intelligence; coa; natural language processing; BERT; XGBoost
Abstract: This research focuses on the detection of Structured Query Language (SQL) injection intrusion detection. This problem has gained significance due to the widespread use of SQL in different systems, as well as for the numerous versions of attacks that are performable by using this technique. This work aims to propose a robust solution for the detection of such attacks by applying artificial intelligence (AI). The data is preprocessed by a Bidirectional Encoder Representations from Transformers (BERT), while the predictions are made by the Extreme Gradient Boosting (XGBoost) algorithm. The XGBoost is a powerful predictor if optimized correctly. Hyperparameters are optimized by an improved version of the Crayfish Optimization Algorithm (COA) hybridized with the Genetic Algorithm (GA). The proposed solution is tested against highperforming metaheuristics in which it achieved favorable performance.
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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Volume Title: Proceedings of the 2nd International Conference on Innovation in Information Technology and Business (ICIITB 2024)
Series: Advances in Computer Science Research
Publication Date: 23 August 2024
ISBN: 978-94-6463-482-2
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-482-2_11 How to use a DOI?
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.

Cite this article

ris enw bib

TY  - CONF
AU  - Aleksandar Jokic
AU  - Nikola Jovic
AU  - Vuk Gajic
AU  - Marina Svicevic
AU  - Milos Pavkovic
AU  - Aleksandar Petrovic
PY  - 2024
DA  - 2024/08/23
TI  - Structured query language injection detection with natural language processing techniques optimized by metaheuristics
BT  - Proceedings of the 2nd International Conference on Innovation in Information Technology and Business (ICIITB 2024)
PB  - Atlantis Press
SP  - 155
EP  - 170
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-482-2_11
DO  - 10.2991/978-94-6463-482-2_11
ID  - Jokic2024
ER  -

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