Deep learning approaches to SQL injection detection: evaluating ANNs, CNNs, and RNNs DOI
Majid Alshammari

Published: Dec. 19, 2023

In the digital era, SQL injection (SQLi) attacks on web applications pose significant threats to data integrity and security. While traditional methods such as signature-based anomaly-based detections have some limitations, this research explores application of neural networks in countering these attacks. Specifically, evaluates performance three primary network architectures: Artificial Neural Networks (ANNs), Convolutional (CNNs), Recurrent (RNNs) for SQLi attack detection. The methodology involves converting text-based queries into numeric values suitable compatible with networks, using Term Frequency-Inverse Document Frequency (TF-IDF), tokenization, padding. Results show that CNN outperforms almost all metrics, RNNs following closely ANNs achieving lower results.

Language: Английский

SQL Injection Attack Detection Using Machine Learning Methods DOI Open Access

Mugisha Ciella Danilla,

Beatrice Dorothy

International Research Journal of Modernization in Engineering Technology and Science, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 27, 2024

SQL injection is a type of technique used by attackers to access database in order modify, delete, copy or store data documents.These attacks can come from many sources, including individuals, groups, and even countries.The goal cyber-attacks damage destroy systems, steal data, hold for ransom one the most online attacks, which typically occurs when an attacker modifies, deletes, reads, copies database.Confidentiality, integrity, these three are areas security where successful compromise.This topic not new area research, though it be crucial as other sources develop attacks.Machine learning artificial intelligence have been tested tried deal with great results.The purpose this paper cover various machine related research detection.Our conducting review inform academic community provide understanding relationship between threats.

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
An Attention Module Integrated Deep Learning Architecture with BERT to Detect SQL Injection Attacks DOI

AsifIqbal Sirmulla,

Prabhakar Manickam

Lecture notes in electrical engineering, Journal Year: 2024, Volume and Issue: unknown, P. 149 - 159

Published: Jan. 1, 2024

Language: Английский

Citations

0
AI capabilities in cybersecurity: detection, prevention and response to SQL injections, XSS, and CSRF attacks DOI

D. E. Vilkhovsky

Matematičeskie struktury i modelirovanie, Journal Year: 2024, Volume and Issue: 4 (72), P. 111 - 111

Published: Dec. 9, 2024

The paper provides an overview of the possibilities using arti cial intelligence to enhance cybersecurity web applications, with emphasis on detecting, preventing, and responding SQL injections, XSS, CSRF attacks. Machine learning methods such as SVM, Naive Bayes, ensemble learning, deep are discussed, well their integration existing security systems. Hybrid models approaches adapting systems new threats included. Existing problems analyzed future research directions for overcoming these challenges identi ed.

Language: Английский

Citations

0
Deep learning approaches to SQL injection detection: evaluating ANNs, CNNs, and RNNs DOI
Majid Alshammari

Published: Dec. 19, 2023

In the digital era, SQL injection (SQLi) attacks on web applications pose significant threats to data integrity and security. While traditional methods such as signature-based anomaly-based detections have some limitations, this research explores application of neural networks in countering these attacks. Specifically, evaluates performance three primary network architectures: Artificial Neural Networks (ANNs), Convolutional (CNNs), Recurrent (RNNs) for SQLi attack detection. The methodology involves converting text-based queries into numeric values suitable compatible with networks, using Term Frequency-Inverse Document Frequency (TF-IDF), tokenization, padding. Results show that CNN outperforms almost all metrics, RNNs following closely ANNs achieving lower results.

Language: Английский

Citations

0