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Effective SQL Injection Detection: A Fusion of Binary Olympiad Optimizer and Classification Algorithm

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  • Bahman Arasteh

    (Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul 34460, Turkey
    Department of Computer Science, Khazar University, Baku AZ1096, Azerbaijan)

  • Asgarali Bouyer

    (Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul 34460, Turkey
    Faculty of Computer Engineering, Azarbaijan Shahid Madani University, Tabriz 5375171379, Iran)

  • Seyed Salar Sefati

    (Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul 34460, Turkey
    Faculty of Electronics, Telecommunications and Information Technology, National University for Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania)

  • Razvan Craciunescu

    (Faculty of Electronics, Telecommunications and Information Technology, National University for Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania)

Abstract

Since SQL injection allows attackers to interact with the database of applications, it is regarded as a significant security problem. By applying machine learning algorithms, SQL injection attacks can be identified. Problem : In the training stage of machine learning methods, effective features are used to develop an optimal classifier that is highly accurate. The specification of the features with the highest efficacy is considered to be an NP-complete combinatorial optimization challenge. Selecting the most effective features refers to the procedure of identifying the smallest and most effective features in the dataset. The rationale behind this paper is to optimize the accuracy, precision, and sensitivity parameters of the SQL injection attack detection method. Method : In this paper, a method for identifying SQL injection attacks was suggested. In the first step, a particular training dataset that included 13 features was developed. In the second step, to specify the best features of the dataset, a specific binary variety of the Olympiad optimization algorithm was developed. Various machine learning algorithms were used to create the optimal attack detector. Results : Based on the experiments carried out, the suggested SQL injection detector using an artificial neural network and the feature selector can achieve 99.35% accuracy, 100% precision, and 100% sensitivity. Owing to selecting about 30% of the effective features, the proposed method enhanced the efficacy of SQL injection detectors.

Suggested Citation

  • Bahman Arasteh & Asgarali Bouyer & Seyed Salar Sefati & Razvan Craciunescu, 2024. "Effective SQL Injection Detection: A Fusion of Binary Olympiad Optimizer and Classification Algorithm," Mathematics, MDPI, vol. 12(18), pages 1-21, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2917-:d:1481397
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    References listed on IDEAS

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    1. Ramachandran, Muthu, 2016. "Software security requirements management as an emerging cloud computing service," International Journal of Information Management, Elsevier, vol. 36(4), pages 580-590.
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