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Comparison study for NLP using machine learning techniques to detecting SQL injection vulnerabilities | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 25، دوره 14، شماره 8، آبان 2023، صفحه 283-290 اصل مقاله (1.03 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.28365.4098 | ||
| نویسندگان | ||
| Manar Hasan Ali AL-Maliki* 1؛ Mahdi Nsaif Jasim2 | ||
| 1Computer Science Department, Informatics Institute for Postgraduate Studies, Iraq | ||
| 2University of Information Technology and Communications, Iraq | ||
| تاریخ دریافت: 23 خرداد 1401، تاریخ بازنگری: 29 تیر 1401، تاریخ پذیرش: 08 شهریور 1401 | ||
| چکیده | ||
| Due to the vast number of electronic attacks that occur on a daily basis, protecting users' data is extremely important in this age of technology. Nowadays, cyber security is regarded as a top priority. Thus, the preservation of user privacy and data security is essential. The SQL vulnerability isn't a new form of website attack; it's been around for a long time. However, it is a new attack nowadays. ML algorithms were used to solve the problem of detecting SQL Injection attacks on websites. By training seven ML algorithms on a batch of data comprising SQL injection queries, including (Naive Bayes, Neural-Network, SVM, Random-Forest, KNN, and Logistic Regression) and choosing the best model that gives the highest accuracy. In comparison to previous studies, high-precision data were obtained, with the Naive-Bayes algorithm achieving 0.99 accuracies, 0.98 precision, 1.00 recall, and a 0.99 f1-score. In this paper, experiences, work schedules, and outcomes are examined. Compared to other methods, this naive Bayes approach has proven to be quite accurate in identifying SQL injection threats. | ||
| کلیدواژه‌ها | ||
| Security؛ Attacks؛ SQL injection؛ Machine learning؛ Deep learning | ||
| مراجع | ||
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