SQL Injection Detection Based on Lightweight Multi-Head Self-Attention
This paper presents a novel neural network model for the detection of Structured Query Language (SQL) injection attacks for web applications. The model features high detection accuracy, fast inference speed, and low weight size. The model is based on a novel Natural Language Processing (NLP) techniq...
Saved in:
Main Authors: | Rui-Teng Lo, Wen-Jyi Hwang, Tsung-Ming Tai |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/15/2/571 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Performance Evaluation of NewSQL Databases in a Distributed Architecture
by: Zhiyao Zhang, et al.
Published: (2025-01-01) -
LotusSQL: SQL Engine for High-Performance Big Data Systems
by: Xiaohan Li, et al.
Published: (2021-12-01) -
FI-NL2PY2SQL: Financial Industry NL2SQL Innovation Model Based on Python and Large Language Model
by: Xiaozheng Du, et al.
Published: (2025-01-01) -
Enhancing aviation control security through ADS-B injection detection using ensemble meta-learning models with Explainable AI
by: Vajratiya Vajrobol, et al.
Published: (2025-01-01) -
Impacts of data consistency levels in cloud-based NoSQL for data-intensive applications
by: Saulo Ferreira, et al.
Published: (2024-11-01)