Ensemble Machine Learning Approaches for Detection of SQL Injection Attack

نویسندگان

چکیده

In the current era, SQL Injection Attack is a serious threat to security of ongoing cyber world particularly for many web applications that reside over internet. Many webpages accept sensitive information (e.g. username, passwords, bank details, etc.) from users and store this in database also resides Despite fact online has much importance remotely accessing by various business purposes but attackers can gain unrestricted access these databases or bypass authentication procedures with help Attack. This attack results great damage variation been ranked as topmost risk OWASP TOP 10. Considering trouble distinguishing unknown attacks principle coordinating technique, strategy injection detection dependent on Machine Learning proposed. Our motive detect splitting queries into their corresponding tokens tokenization then applying our algorithms tokenized dataset. We used four Ensemble algorithms: Gradient Boosting (GBM), Adaptive (AdaBoost), Extended (XGBM), Light (LGBM). The yielded models are near perfection error rate being almost negligible. best LGBM an accuracy 0.993371, precision, recall, f1 0.993373, 0.993370, respectively. less False Positive Rate (FPR) Root Mean Squared Error (RMSE) be 0.120761 0.007, worst AdaBoost 0.991098, 0.990733, 0.989175, 0.989942, high 0.009.

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ژورنال

عنوان ژورنال: Tehni?ki glasnik

سال: 2021

ISSN: ['1846-6168', '1848-5588']

DOI: https://doi.org/10.31803/tg-20210205101347