A Dual-Layer AI-Driven Cybersecurity Framework for Healthcare: Integrating Offensive and Defensive Strategies in Smart Medical Systems - Novagusda, Fahrul Nizar (Universitas Nusa Putra, 2025)
As healthcare systems become increasingly digital and interconnected, the need for smarter and more adaptive cybersecurity is more urgent than ever. This study presents a dual-layer cybersecurity framework powered by artificial intelligence, specifically designed to address the complex security challenges in modern medical environments. The proposed approach combines both defensive strategies—such as data protection, threat monitoring, and regulatory compliance—with offensive strategies that mimic cyberattacks to identify system vulnerabilities before real threats can exploit them. Built on big data, machine learning, and deep learning technologies, the framework enables real-time detection, response, and continuous adaptation to evolving threats. The system architecture includes multi-layered protection, intelligent intrusion detection, and AI-driven simulation of attacker behavior. This paper also explores how the framework supports resilience and flexibility in healthcare cybersecurity, especially in critical areas like diagnostics, telemedicine, and medical device integration. Our findings highlight the potential of this AI-integrated approach to enhance the security of electromedical systems and contribute to more robust, future-ready healthcare infrastructures.