Advanced deep learning framework for detecting SQL injection attacks based on GRU Model DOI Creative Commons

Oussama Senouci,

Nadjib Benaouda

STUDIES IN ENGINEERING AND EXACT SCIENCES, Journal Year: 2024, Volume and Issue: 5(2), P. e11299 - e11299

Published: Nov. 29, 2024

SQL injection attacks are a major danger to data security in application systems, leveraging weaknesses illicitly access and change sensitive data. Traditional detection methods, such rule-based systems supervised machine learning, struggle adapt new attack strategies. This study presents an Enhanced Deep Learning Framework for Injection Detection utilizing the Gated Recurrent Unit (GRU) model overcome constraints. To discover patterns, proposed framework uses dynamic learning process instead of static methods. By examining query sequences, can distinguish between legal malicious interactions without predefined rules or reinforcement learning. The framework's performance is assessed using broad dataset valid queries. Experiments show considerable increase accuracy, reaching 96.65% with little false positives. system resilient adaptable address complexity modern threats. results demonstrate effectiveness deep particularly GRU model, detecting attacks. research enhances database lays groundwork future cyber-security methods web-based applications.

Language: Английский

UniEmbed: A Novel Approach to Detect XSS and SQL Injection Attacks Leveraging Multiple Feature Fusion with Machine Learning Techniques DOI Creative Commons
Rezan Bakır

Arabian Journal for Science and Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 12, 2025

Language: Английский

Citations

1
Sql injection detection algorithm based on Bi-LSTM and integrated feature selection DOI

Qiurong Qin,

Yueqin Li,

Yajie Mi

et al.

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(4)

Published: March 12, 2025

Language: Английский

Citations

0
Adaptive protocols for hypervisor security in cloud infrastructure using federated learning-based anomaly detection DOI
Moutaz Alazab, Albara Awajan,

Areej Obeidat

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 152, P. 110750 - 110750

Published: April 15, 2025

Language: Английский

Citations

0
Research on SQL Injection Detection Method Based on Mixed Word Embedding DOI

Ning Xu,

Dalong Zhang,

Baozhan Chen

et al.

2022 4th International Conference on Communications, Information System and Computer Engineering (CISCE), Journal Year: 2024, Volume and Issue: 50, P. 995 - 998

Published: May 10, 2024

Language: Английский

Citations

0
Advanced deep learning framework for detecting SQL injection attacks based on GRU Model DOI Creative Commons

Oussama Senouci,

Nadjib Benaouda

STUDIES IN ENGINEERING AND EXACT SCIENCES, Journal Year: 2024, Volume and Issue: 5(2), P. e11299 - e11299

Published: Nov. 29, 2024

SQL injection attacks are a major danger to data security in application systems, leveraging weaknesses illicitly access and change sensitive data. Traditional detection methods, such rule-based systems supervised machine learning, struggle adapt new attack strategies. This study presents an Enhanced Deep Learning Framework for Injection Detection utilizing the Gated Recurrent Unit (GRU) model overcome constraints. To discover patterns, proposed framework uses dynamic learning process instead of static methods. By examining query sequences, can distinguish between legal malicious interactions without predefined rules or reinforcement learning. The framework's performance is assessed using broad dataset valid queries. Experiments show considerable increase accuracy, reaching 96.65% with little false positives. system resilient adaptable address complexity modern threats. results demonstrate effectiveness deep particularly GRU model, detecting attacks. research enhances database lays groundwork future cyber-security methods web-based applications.

Language: Английский

Citations

0