Comparative Analysis of SQL Injection Attack Clasification Using Naïve Bayes Method And Support Vector Machine (SVM).
DOI:
https://doi.org/10.47701/icohetech.v5i1.4178Keywords:
Sql Injection, Classification, Naïve Bayes, Support Vector MachineAbstract
SQL Injection is an attack that attempts to gain unauthorized access to a database by injecting code and exploiting SQL queries. SQL injection is an attack that is easy to execute but difficult to detect and classify because of the many types. The SQLI vulnerability is the result of incorrect validation of user input, enabling attackers to manipulate programmer queries by adding new SQL operators. Therefore, this study compares the use of the Naïve Bayes algorithm with the Support Vector Machine (SVM). The dataset that will be used in this study comes from a website called Kaggle. This study analyzes the comparison of methods resulting from the classification process based on the value of accuracy of confusion matrix, precision, recall. Naive Bayes, 95.594% accuracy quality while Support Vector Machine (SVM) 96.093% accuracy quality. The highest percentage of accuracy is obtained by the Support Vector Machine (SVM) while the Naïve Bayes accuracy score is slightly lower.
References
Alghawazi, M., Alghazzawi, D., & Alarifi, S. (2022). Detection of SQL Injection Attack Using Machine Learning Techniques: A Systematic Literature Review. Journal of Cybersecurity and Privacy, 2(4), 764–777. https://doi.org/10.3390/jcp2040039
Arum Sari, A. (n.d.). Prediksi Serangan Sql Injection Pada Jaringan Komputer Menggunakan Metode Support Vector Machine (SVM). https://www.kaggle.com/datasets/syedsaqlainhussain/sql-injection-dataset
Barella, Y., Fergina, A., Mustami, M. K., Rahman, U., & Alajaili, H. M. A. (2024). Quantitative Methods in Scientific Research. Jurnal Pendidikan Sosiologi Dan Humaniora, 15(1), 281. https://doi.org/10.26418/j-psh.v15i1.71528
Johny, J. H. B., Nordin, W. A. F. B., Lahapi, N. M. B., & Leau, Y. B. (2021). SQL Injection Prevention in Web Application: A Review. Communications in Computer and Information Science, 1487 CCIS, 568–585. https://doi.org/10.1007/978-981-16-8059-5_35
Jony, A. I., & Hamim, S. A. (2024). Navigating the Cyber Threat Landscape: A Comprehensive Analysis of Attacks and Security in the Digital Age. Journal of Information Technology and Cyber Security, 1(2), 53–67. https://doi.org/10.30996/jitcs.9715
Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019). Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity, 2(1). https://doi.org/10.1186/s42400-019-0038-7
Pusean, N. V., Charibaldi, N., & Santosa, B. (2023). Comparison of Scenario Pre-processing Performance on Support Vector Machine and Naïve Bayes Algorithms for Sentiment Analysis. Inform?: Jurnal Ilmiah Bidang Teknologi Informasi Dan Komunikasi, 8(1), 57–63. https://doi.org/10.25139/inform.v8i1.5667
Rimal, Y. (2019). INTERNATIONAL JOURNAL ON ORANGE TECHNOLOGIES (IJOT) e-Naïve Bayes Machine Learning Classification with R Programming: A case study of binary data sets. www.researchparks.org
Siino, M., Tinnirello, I., & La Cascia, M. (2024). Is text preprocessing still worth the time? A comparative survey on the influence of popular preprocessing methods on Transformers and traditional classifiers. Information Systems, 121. https://doi.org/10.1016/j.is.2023.102342
Syahputra, R., Yanris, G. J., & Irmayani, D. (2022). SVM and Naïve Bayes Algorithm Comparison for User Sentiment Analysis on Twitter. Sinkron, 7(2), 671–678. https://doi.org/10.33395/sinkron.v7i2.11430
https://www.kaggle.com/datasets/syedsaqlainhussain/sql-injection-dataset
