A lightweight trusted framework for secure data exchange and threat mitigation in IoT-enabled healthcare environments

Area of Science:

• Computer Science
• Cybersecurity
• Healthcare Technology

Background:

• The Internet of Things (IoT) in healthcare offers advanced patient monitoring and decision-making.
• However, IoT adoption introduces significant security and privacy vulnerabilities.
• Existing solutions often lack efficiency or robustness for constrained healthcare devices.

Purpose of the Study:

• To propose SecHealth, a lightweight trusted framework for secure data exchange and threat mitigation in healthcare IoT systems.
• To enhance data confidentiality, integrity, and trust in IoT-enabled healthcare environments.
• To address security challenges posed by both internal and external threats.

Main Methods:

• Developed a multi-layered trust management mechanism using behavioral, communication, and contextual parameters.
• Implemented a lightweight Elliptic Curve Cryptography-based encryption protocol (LECCEP-A) for low-latency data transfer.
• Integrated a hybrid anomaly detection system (RHADS) combining machine learning (LSTM, VAE, SVM) and probabilistic reasoning.

Main Results:

• SecHealth achieved high performance in a simulated healthcare IoT network (100-500 devices).
• Demonstrated anomaly detection accuracy of 98.1% with a low false positive rate (FPR) of 2.1%.
• Achieved low latency (85-95 ms), good energy efficiency (0.68-0.78 J/node), and throughput (155-180 Kbps), outperforming benchmark models.

Conclusions:

• SecHealth effectively mitigates internal and external threats in healthcare IoT systems.
• The framework ensures data confidentiality, integrity, and trust.
• Its flexible and scalable design is suitable for real-world healthcare infrastructures with resource-constrained devices.