@Article{crph1333,
AUTHOR = {Vadisetty, Rahul and Chinta, Purna Chandra Rao and Moore, Chethan Sriharsha and Karaka, Laxmana Murthy and Sakuru, Manikanth and Bodepudi, Varun and Maka, Srinivasa Rao and Vangala, Srikanth Reddy},
TITLE = {Intelligent Detection of Injection Attacks via SQL Based on Supervised Machine Learning Models for Enhancing Web Security},
JOURNAL = {Current Research in Public Health},
VOLUME = {4},
YEAR = {2024},
NUMBER = {2},
PAGES = {109-119},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1333},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2024.1333},
ABSTRACT = {The most prevalent technique behind security data breaches exists through SQL Injection Attacks. Organizations and individuals suffer from sensitive information exposure and unauthorized entry when attackers take advantage of SQL injection (SQLi) attack vulnerability’s severe risks. Static and heuristic defense methods remain conventional detection tools for previous SQL injection attacks study's foundation is a detection system developed using the Gated Recurrent Unit (GRU) network, which attempts to efficiently identify SQL Injection attacks (SQLIAs). The suggested Gated Recurrent Unit model was trained using an 80:20 train-test split, and the results showed that SQL injection attacks could be accurately identified with a precision rate of 97%, an accuracy rate of 96.65%, a recall rate of 92.5%, and an F1-score of 94%. The experimental results, together with their corresponding confusion matrix analysis and learning curves, demonstrate resilience and outstanding generalization ability. The GRU model outperforms conventional machine learning (ML) models, including K-Nearest Neighbor’s (KNN), and Support Vector Machine (SVM), in terms of identifying sequential patterns in SQL query data. Recurrent neural architecture proves effective in the detection of SQLi attacks through its ability to provide secure protection for contemporary web applications.},
}

%0 Journal Article
%A Vadisetty, Rahul
%A Chinta, Purna Chandra Rao
%A Moore, Chethan Sriharsha
%A Karaka, Laxmana Murthy
%A Sakuru, Manikanth
%A Bodepudi, Varun
%A Maka, Srinivasa Rao
%A Vangala, Srikanth Reddy
%D 2024
%J Current Research in Public Health

%@ 2831-5162
%V 4
%N 2
%P 109-119

%T Intelligent Detection of Injection Attacks via SQL Based on Supervised Machine Learning Models for Enhancing Web Security
%M doi:10.31586/jaibd.2024.1333
%U https://www.scipublications.com/journal/index.php/JAIBD/article/view/1333

TY  - JOUR
AU  - Vadisetty, Rahul
AU  - Chinta, Purna Chandra Rao
AU  - Moore, Chethan Sriharsha
AU  - Karaka, Laxmana Murthy
AU  - Sakuru, Manikanth
AU  - Bodepudi, Varun
AU  - Maka, Srinivasa Rao
AU  - Vangala, Srikanth Reddy
TI  - Intelligent Detection of Injection Attacks via SQL Based on Supervised Machine Learning Models for Enhancing Web Security
T2  - Current Research in Public Health
PY  - 2024
VL  - 4
IS  - 2
SN  - 2831-5162
SP  - 109
EP  - 119
UR  - https://www.scipublications.com/journal/index.php/JAIBD/article/view/1333
AB  - The most prevalent technique behind security data breaches exists through SQL Injection Attacks. Organizations and individuals suffer from sensitive information exposure and unauthorized entry when attackers take advantage of SQL injection (SQLi) attack vulnerability’s severe risks. Static and heuristic defense methods remain conventional detection tools for previous SQL injection attacks study's foundation is a detection system developed using the Gated Recurrent Unit (GRU) network, which attempts to efficiently identify SQL Injection attacks (SQLIAs). The suggested Gated Recurrent Unit model was trained using an 80:20 train-test split, and the results showed that SQL injection attacks could be accurately identified with a precision rate of 97%, an accuracy rate of 96.65%, a recall rate of 92.5%, and an F1-score of 94%. The experimental results, together with their corresponding confusion matrix analysis and learning curves, demonstrate resilience and outstanding generalization ability. The GRU model outperforms conventional machine learning (ML) models, including K-Nearest Neighbor’s (KNN), and Support Vector Machine (SVM), in terms of identifying sequential patterns in SQL query data. Recurrent neural architecture proves effective in the detection of SQLi attacks through its ability to provide secure protection for contemporary web applications.
DO  - Intelligent Detection of Injection Attacks via SQL Based on Supervised Machine Learning Models for Enhancing Web Security
TI  - 10.31586/jaibd.2024.1333
ER  -