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

Big data analytics deep learning techniques and applications: A survey DOI

Hend A. Selmy,

Hoda K. Mohamed,

Walaa Medhat

et al.

Information Systems, Journal Year: 2023, Volume and Issue: 120, P. 102318 - 102318

Published: Nov. 21, 2023

Language: Английский

Citations

23
AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection DOI Creative Commons
Nisrean Thalji, Ali Raza, Mohammad Shariful Islam

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 135507 - 135516

Published: Jan. 1, 2023

Structured Query Language (SQL) injection attacks represent a critical threat to database-driven applications and systems, exploiting vulnerabilities in input fields inject malicious SQL code into database queries. This unauthorized access enables attackers manipulate, retrieve, or even delete sensitive data. The through underscores the importance of robust Artificial Intelligence (AI) based security measures safeguard against attacks. study's primary objective is automated timely detection AI without human intervention. Utilizing preprocessed 46,392 queries, we introduce novel optimized approach, Autoencoder network (AE-Net), for automatic feature engineering. proposed AE-Net extracts new high-level deep features from textual data, subsequently machine learning models performance evaluations. Extensive experimental evaluation reveals that extreme gradient boosting classifier outperforms existing studies with an impressive k-fold accuracy score 0.99 detection. Each applied approach's further enhanced hyperparameter tuning validated via cross-validation. Additionally, statistical t-test analysis assess variations. Our innovative research has potential revolutionize attacks, benefiting specialists organizations.

Language: Английский

Citations

14
Analysis of SQL injection attacks in the cloud and in WEB applications DOI
Animesh Kumar,

Sandip Dutta,

Prashant Pranav

et al.

Security and Privacy, Journal Year: 2024, Volume and Issue: 7(3)

Published: Jan. 18, 2024

Abstract Cloud computing has revolutionized the way IT industries work. Most modern‐day companies rely on cloud services to accomplish their day‐to‐day tasks. From hosting websites developing platforms and storing resources, tremendous use in modern information technology industry. Although an emerging technique, it many security challenges. In structured query language injection attacks, attacker modifies some parts of user still sensitive information. This type attack is challenging detect prevent. this article, we have reviewed 65 research articles that address issue its prevention detection Traditional Networks, which 11 are related general rest 54 specifically web security. Our result shows Random Forest accuracy 99.8% a Precision rate 99.9%, worst‐performing model Multi‐Layer Perceptron (MLP) SQLIA Model. For recall value, performs best while TensorFlow Linear Classifier worst. F1 score Forest, MLP most diminutive performer.

Language: Английский

Citations

5
An Effective SQL Injection Detection Model Using LSTM for Imbalanced Datasets DOI
Khaled Salah, Sherif Barakat, Amira Rezk

et al.

Computers & Security, Journal Year: 2025, Volume and Issue: unknown, P. 104391 - 104391

Published: Feb. 1, 2025

Language: Английский

Citations

0
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
Detecting and Mitigating SQL Injection in .NET Applications Using AI-Based Anomaly Detection DOI

Sohan Singh Chinthalapudi

Published: April 11, 2025

SQL Injection (SQLi) persists as a major threat to .NET applications since attackers can inject harmful code into databases for database manipulation purposes. The presence of this vulnerability leads hackers gaining access unauthorized data and causing system integrity failure while resulting in lost which threatens organizations utilizing these applications. Signature-based detection systems demonstrate limited effectiveness when it comes detecting contemporary or innovative SQLi attacks that create new patterns. Artificial Intelligence through anomaly technology provides capable defensive solution overcome particular challenge. normal behavior patterns queries inside become manageable AI machine learning algorithms detect abnormal signal attack vulnerabilities. research introduces specific AI-based designed application environments. Our method begins with collecting query logs then performing preprocessing before extracting important features are used train model between valid hostile queries. process relies on an RNN autoencoder understands sequences thus identifying anomalous related injection. Experimental testing shows the proposed reaches high precision alongside minimal false alarms recognized well unrecognized attacks. security position becomes more robust implementation protecting against current future

Language: Английский

Citations

0
A Machine Learning Approach to SQL Injection Detection in Web Applications DOI

Youssef Seada,

Ahmed Mohamed,

Mena Hany

et al.

Published: July 13, 2024

Language: Английский

Citations

1
Feature Fusion-Based Detection of SQL Injection and XSS Attacks DOI

Yinfan Guan,

Wenrui Zhou,

Huiling Wang

et al.

Published: May 31, 2024

Language: Английский

Citations

1
Machine Learning-Based Detection and Mitigation of XML SQL Injection Attacks DOI

Vanshika Pahuja,

Rajat Dubey,

Ishu Sharma

et al.

Published: Dec. 1, 2023

XML and SQL injection attacks are occurring very frequently nowadays as developers lack major security measures awareness for the purpose of securing web applications documents. Many factors responsible these types vulnerabilities main reasons behind all like urbanisation, environmental degradation some market conditions in lead to such attacks. These may also allow attackers gain advantage overlook front end application by taking input fields usernames, passwords, important credentials. This research paper covers detailed study occurrence on development applications, detection mitigation policies employed literature. The machine learning based model is presented this case detecting discussed. classification compared using metrics accuracy, precision, Specificity F-1 Score.

Language: Английский

Citations

1
Securing transportation web applications: An AI-driven approach to detect and mitigate SQL injection attacks DOI
Nachaat Mohamed

Journal of Transportation Security, Journal Year: 2024, Volume and Issue: 17(1)

Published: Jan. 8, 2024

Language: Английский

Citations

0