2026-05-09T16:19:21.620Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:10127 2024-08-26T12:52:57Z 3

Structured query language injection detection with natural language processing techniques optimized by metaheuristics 2024 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/10127 https://doi.org/10.2991/978-94-6463-482-2_11 A. Jokic N. Jovic V. Gajic M. Svicevic M. Pavkovic A. Petrovic 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. bookPart Atlantis Press 155 170 10.2991/978-94-6463-482-2_11 Proceedings of the 2nd International Conference on Innovation in Information Technology and Business (ICIITB 2024), Chapter in Advances in Computer Science Research