A Secure Prescription System with Machine Learning for SQL Injection Detection

Abstract

This research introduces a secure, web-based prescription system designed to monitor antibiotic consumption and reduce the misuse of critical antibiotics in clinical environments. The system's user interface supports structured documentation and justification of antibiotic use, serving as a clinical surveillance tool that promotes responsible prescribing and contributes to the prevention of hospital-acquired infections through improved antimicrobial stewardship. To ensure robust data protection, the system was evaluated under simulated cyberattacks, including unauthorized access, Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), and SQL injection attacks. In addition to standard security mechanisms such as Transport Layer Security (TLS) and Elliptic Curve Cryptography (ECC), the system integrates a machine learning–based module implemented in Python to enhance real-time SQL injection detection. The module leverages supervised learning algorithms to classify database queries as malicious or safe, enabling proactive defense against threats targeting sensitive medical records. By embedding machine learning into a secure clinical workflow, the system supports sustainable antibiotic management in hospitals, laying a foundation for scalable, intelligent, and secure e-health infrastructures.