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September 10, 2025SensorsOpen Access

Intrusion Detection and Real-Time Adaptive Security in Medical IoT Using a Cyber-Physical System Design

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Authors

FAFaeiz Alserhani

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Overview

This research demonstrates an ML-enabled framework for intrusion detection in medical IoT, highlighting its adaptive security features and high reliability.

Key Points

  • The ML-enabled cognitive cyber-physical system achieves a macro F1-score of 97.8%, demonstrating effective intrusion detection in MIoT environments.
  • Using extreme learning machine-based classification, the framework ensures reliable monitoring and adaptive access control under various conditions.
  • The system excels in resource-constrained settings, maintaining strong performance even when faced with noisy telemetry data.
  • Comparative evaluations affirm the system's superior capability to detect novel cyber threats in real-time medical environments.

Cite This Study

Faeiz Alserhani (2025) studied this question.

synapsesocial.com/papers/68c240deb210217d6479cb00https://doi.org/10.3390/s25154720
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  4. 4A Dual-Layer AI-Driven Cybersecurity Framework for Healthcare: Integrating Offensive and Defensive Strategies in Smart Medical Systems2025
  5. 5Advancing IoT Cybersecurity through AI and ML: A Comparative Study on Intrusion Detection and Privacy Protection2025 · 3 citations