SQL Injection Attack Detection using ResNet

Sangeeta, .; Nagasundari, S; Honnavali, Prasad B

doi:10.1109/icccnt45670.2019.8944874

Cited by 6 publications

(1 citation statement)

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“…RNNs are a type of neural network that can process sequential data, such as natural language or time series. RNNs have been shown to be effective in capturing the temporal dependencies and semantic features of sequential data, making them well-suited for analyzing SQL queries (ArunKumar et al, 2021;Nagasundari & Honnavali, 2019).…”

Section: Introductionmentioning

confidence: 99%

SQL Injection Detection Using RNN Deep Learning Model

ALAzzawi

2023

JAETS

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SQL injection attacks are a common type of cyber-attack that exploit vulnerabilities in web applications to access databases through malicious SQL queries. These attacks pose a serious threat to the security and integrity of web applications and their data. The existing methods for detecting SQL injection attacks are based on predefined rules that can be easily circumvented by sophisticated attackers. Therefore, there is a need for a more robust and effective method for detecting SQL injection attacks. In this research, we propose a novel method for detecting SQL injection attacks using recurrent neural networks (RNN), which are a type of deep learning model that can capture the syntax and semantic features of SQL queries. We train an RNN model on a dataset of benign and malicious SQL queries, and use it to classify queries as either benign or malicious. We evaluate our method on a benchmark dataset and compare it with the existing rule-based methods. Our experimental results show that our method achieved high accuracy and outperformed the rule-based methods for detecting SQL injection attacks. Our research contributes to the field of web application security by providing a new and effective solution for protecting web applications from SQL injection attacks using deep learning. Our method has both practical and theoretical implications, as it can be easily integrated into existing web application security frameworks to provide an additional layer of protection against SQL injection attacks, and it can also advance the understanding of how deep learning models can be applied to natural language processing tasks such as SQL query analysis.

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Section: Introductionmentioning

confidence: 99%