Abstract
A drastic increase in cyber-attacks affects internet reliability in different fields. The website Structured Query Language (SQL) Injection is still a loophole that attackers frequently use. Four algorithms have been implemented and tested in this study: Logistic Regression, Naive Bayes, Random Forest and Convolutional Neural Network (CNN). Natural Language Processing (NLP) is also applied to increase the text processing's detection accuracy. The study is made using two datasets for preparing the model. The data for the model that is used for training are contained in the first dataset. The validation records used to test and evaluate the model are contained in the second dataset. The primary dataset is turned into a corpus to help the model comprehend the input more quickly and easily. Based on the performance of the models, CNN has given the highest level of accuracy. The response time of the output is less, and the validation accuracy is 99.29%. Compared to machine learning methods, the Deep Learning (DL) algorithm performed far better. The accuracy of Machine Learning (ML) approaches is 0.942857 for Logistic Regression, 0.978576 for Naive Bayes, and 0.902380 for Random Forest.
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Natarajan, Y., Karthikeyan, B., Wadhwa, G., Srinivasan, S.A., Akilesh, A.S.P. (2023). A Deep Learning Based Natural Language Processing Approach for Detecting SQL Injection Attack. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 715. Springer, Cham. https://doi.org/10.1007/978-3-031-35507-3_38
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