Abstract :

SQL Injection (SQLi) Remains One Of The Most Pervasive And Damaging Web Security Threats, Demanding Advanced And Scalable Detection Strategies Beyond Traditional Rule-based Filters. This Research Proposes A Unified, Cloud-enabled Machine Learning Framework For Predictive SQL Injection Detection And Prevention Across AWS, Microsoft Azure, And Google Cloud Platform (GCP). The Study Leverages Supervised And Unsupervised Learning Models To Analyze Query Behavior, Extract Anomalous Patterns, And Classify Malicious Injection Attempts In Real Time. Cloud-native Services Such As AWS SageMaker, Azure Machine Learning, And Google Vertex AI Are Incorporated To Train, Deploy, And Monitor Scalable Models With Distributed Processing. The Proposed Architecture Integrates API Gateways, Serverless Functions, And Managed Databases To Ensure Seamless Ingestion And Protection Across Multi-cloud Environments. Experimental Evaluation Demonstrates High Precision And Recall, Outperforming Signaturebased Systems In Detecting Zero-day SQLi Variants. The Results Indicate That Predictive Analytics Combined With Multi-cloud AI Deployment Significantly Enhances Resilience, Adaptability, And Response Time. This Framework Provides A Scalable Path Toward Intelligent Intrusion Prevention For Modern Cloud-hosted Applications. Keywords: SQL Injection, Machine Learning, Predictive Analytics, Cloud Security, AWS, Azure, Google Cloud Platform (GCP), Intrusion Detection, Cybersecurity, Multi-Cloud Defense.

Published:

16-8-2022

Issue:

Vol. 22 No. 8 (2022)

Page Nos:

68-74

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite