A Cybersecurity Framework for Optimizing Broadband QoS in IoT Systems Using Machine Learning

Abstract

The integration of Internet of Things (IoT) technologies in healthcare, particularly in Intensive Care Units (ICUs), holds transformative potential for patient monitoring and clinical decision-making. However, performance is often limited by high network latency and cybersecurity vulnerabilities, which are especially critical in time-sensitive applications such as remote monitoring and telemedicine. Achieving both ultra-low latency and strong data confidentiality in resource-constrained ICU environments remains a major challenge, as traditional methods fall short of meeting these dual requirements. This paper proposes a machine learning (ML)-driven cybersecurity framework that optimizes broadband Quality of Service (QoS) while ensuring robust data security in ICU-based IoT networks. The framework integrates supervised and unsupervised learning models for dynamic, context-aware adaptation to network conditions and emerging threats. Key features include intelligent traffic prioritization, secure communication protocols, and adaptive bandwidth allocation. Expected outcomes are reduced latency, improved confidentiality, and enhanced reliability of ICU systems. Beyond technical contributions, the framework promotes trust in digital healthcare and advances interdisciplinary research across ML, network optimization, and medical cybersecurity.