A Survey on SQL Injection Detection

Deeksha M B , Dr. Kavyasri M N , Anusha K R , Harshil K , Aniketh Patil

SQL Injection, Anomaly Detection, Convolutional Neural Networks (CNN), Multi-Layer Perceptron (MLP), Word Embedding, Malicious Query.

The increasing reliance on web-based applications and databases has increased the risk of SQL Injection Attacks (SQLIAs), which is one of the most widespread threats to web platforms. SQLIAs take advantage of weaknesses in user input validation to run malicious SQL queries, leading to unauthorized access, data theft, and extreme economic loss. In response to these issues, many research studies have looked into machine learning (ML) and deep learning (DL) solutions to improve SQLIA detection. Methods like 2D-Convolutional Neural Networks (2D-CNNs), Elastic- Pooling CNNs, Multi-Layer Perceptrons (MLPs), and hybrid approaches like CNN-BiLSTM have shown higher accuracy and adaptability compared to conventional rule-based systems. These models utilize features like word embeddings, syntactic parsing, and vector representations to detect SQLIA patterns efficiently. Using Kaggle and other data sets, experiments show the resilience of DL models in real-time detection and capacity to classify intricate SQLIAs, guaranteeing a substantial improvement in protecting database-driven systems.

Published Paper   E-Certificate

"A Survey on SQL Injection Detection", IJNTI - INTERNATIONAL JOURNAL OF NOVEL TRENDS AND INNOVATION (www.IJNTI.org), ISSN:2984-908X, Vol.3, Issue 5, page no.a252-a255, May-2025, Available :https://rjpn.org/IJNTI/papers/IJNTI2505029.pdf

Volume 3 Issue 5, May-2025

Pages : a252-a255

Paper Reg. ID: IJNTI_231305

Published Paper Id: IJNTI2505029

Downloads: 0001062

Research Area: Science and Technology

Country: Hassan, Karnataka, India

Published Paper PDF: https://rjpn.org/IJNTI/papers/IJNTI2505029

Published Paper URL: https://rjpn.org/IJNTI/viewpaperforall?paper=IJNTI2505029

DOI: https://doi.org/10.56975/ijnti.v3i5.231305

ISSN: 2984-908X | IMPACT FACTOR: 9.57 Calculated By Google Scholar | ESTD YEAR: 2023

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: RJPN (IJPublication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition