Omim, Shahamat and Khan, Hafiz Aziz and Khan, MD Minar and Soumik, Md Shadman and oumik, Chowdhury Amin Abdullah and Afrin, Mehenaz and Al Mamun, Abdullah and Sarkar, Mrinmoy (2025) Enhancing Cyber security in Medical IoT Devices Using AI-Based Data Analytics Approaches. International Journal of Communication Networks and Information Security (IJCNIS), 17 (8). pp. 111-160. ISSN 2073-607X

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Abstract

The recent surge in interconnected Medical Internet of Things (MIoT) gadgets has revolutionized the healthcare delivery process, yet it has brought about sophisticated cybersecurity problems. The wearable input sensors and implantable medical devices are MIoT systems that generate and transmit sensitive health information, and are susceptible to malware, ransomware, and data interception attacks. The traditional rule-based security controls cannot adequately deal with the dynamic threats, which will take advantage of the heterogeneity and limited computing capabilities of MIoT devices. The current paper discusses the opportunities of Artificial Intelligence (AI)-based data analytics to improve MIoT cybersecurity. It is possible to detect, categorize, and mitigate cyber threats within seconds, as AI models based on machine learning (ML), deep learning (DL), and anomaly detection algorithms can recognize them. The paper has reviewed available literature about AI based intrusion detection, analyzed performance indicators like detection rate, false positive rate, and latency, and outlined a conceptual architecture of predictive analytics and federated learning to guarantee privacy in defense. The results reveal that AI analytics would play an important role in the resilience, trust, and transparency of MIoT systems. The article ends with a discussion of the future research direction such as explainable AI (XAI), edge computing integration, and regulatory compliance as key strategies to provide a safe healthcare environment.

Item Type:	Article
Uncontrolled Keywords:	Medical Internet of Things (MIoT); Cyber security; Artificial Intelligence; Data Analytics; Machine Learning; Deep Learning; Anomaly Detection; Healthcare Technology; Federated Learning
Subjects:	A General Works > AI Indexes (General)
Depositing User:	admin eprints
Date Deposited:	13 Oct 2026 01:49
Last Modified:	13 Oct 2026 01:49
URI:	http://eprints.umsida.ac.id/id/eprint/17203

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