Abstract :

The Rapid Digital Transformation Of Healthcare Has Enabled Continuous Communication Among Patients, Physicians, Hospitals, Laboratories, Pharmacies, Wearable Devices, Telemedicine Platforms, Mobile Health Applications, Cloud Infrastructures, And Electronic Health Record Systems. Although These Technologies Improve Accessibility, Clinical Coordination, Remote Consultation, And Real-time Health Information Exchange, They Also Create Significant Cybersecurity Risks Involving Phishing, Credential Theft, Ransomware, Malicious Communication, Unauthorized Access, Data Leakage, Identity Compromise, Message Manipulation, Insider Threats, Adversarial Attacks, And Privacy Violations. Conventional Healthcare Cybersecurity Mechanisms Frequently Depend On Static Access Rules, Signature-based Threat Detection, Fixed Authentication Policies, And Isolated Communication Controls That May Not Identify Rapidly Evolving Threats Across Heterogeneous Digital Health Environments. This Research Proposes An Intelligent Cyber Security AI-Driven Digital Health Communication Framework That Integrates Artificial Intelligence, Machine Learning, Natural Language Processing, Behavioral Analytics, Anomaly Detection, Adaptive Access Control, Secure Communication, Threat Intelligence, And Continuous Monitoring Within A Unified Healthcare Cybersecurity Architecture. The Proposed System Continuously Collects Communication Metadata, Authentication Events, Device Characteristics, Network Activity, User Behavior, Message Content Features, Application Logs, API Transactions, Cloud Events, And Healthcare Resource-access Patterns. Random Forest, Support Vector Machine, XGBoost, Isolation Forest, And Deep Neural Models Are Employed To Identify Malicious Communication, Abnormal Behavior, Compromised Accounts, Suspicious Access Attempts, And Previously Unseen Anomalies. Natural Language Processing Analyzes Digital Health Messages For Phishing Indicators, Social-engineering Patterns, Malicious URLs, Impersonation Attempts, And Suspicious Linguistic Characteristics. A Hybrid Security Decision Engine Combines AI Predictions, Anomaly Severity, Identity Confidence, Device Trust, Communication Sensitivity, And Healthcare Resource Criticality To Classify Events As Normal, Suspicious, High Risk, Or Critical Threat. The Proposed Architecture Consists Of Five Interconnected Layers: Digital Health Communication And Data Acquisition, Security Preprocessing And Contextual Intelligence, AIDriven Cyber Threat Detection, Risk Assessment And Adaptive Security Enforcement, And Secure Healthcare Application And User Layers. Illustrative Conceptual Evaluation Demonstrates Improved Detection Accuracy, Precision, Recall, F1-score, Communication-security Efficiency, And Analytical Response Time Compared With Traditional Rule-based Security, Signature-based Intrusion Detection, And Conventional Machine Learning Approaches. The Framework Provides A Scalable Foundation For Protecting Telemedicine, Electronic Health Records, Mobile Health Applications, Hospital Communication Platforms, Wearable Healthcare Systems, Cloud-based Medical Services, And Connected Digital Health Ecosystems.

Published:

09-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

344-355

Section:

Articles

License:

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

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