A threat intelligence framework for protecting smart satellite-based healthcare networks

Area of Science:

• Cybersecurity
• Network Protocols
• Healthcare Technology

Background:

• Human-to-machine (H2M) communication is crucial in the Industrial Internet of Health Things (IIoHT).
• Lightweight protocols like Constrained Application Protocol (CoAP) are used in smart satellite-based healthcare IoT networks (SmartSat-IIoHT) for medical device data transfer.
• Inadequate security configurations in CoAP expose these systems to cyber threats.

Purpose of the Study:

• To present a novel threat intelligence framework for examining and modeling CoAP protocol attacks within IIoHT systems.
• To introduce and analyze a new Ransom Denial of Service (RDoS) attack exploiting CoAP vulnerabilities.
• To develop and evaluate a deep learning model for real-time detection of RDoS attacks in SmartSat-IIoHT networks.

Main Methods:

• Development of a threat intelligence framework to model CoAP attacks.
• Proposal and analysis of Ransom Denial of Service (RDoS) attack techniques.
• Implementation of a deep learning model for real-time network behavior analysis and attack discovery.

Main Results:

• The proposed framework effectively models CoAP protocol attacks, including the novel RDoS threat.
• The deep learning model demonstrates superior performance in detecting RDoS attacks compared to conventional machine learning algorithms.
• High fidelity in protecting SmartSat-IIoHT networks against identified RDoS attacks was achieved.

Conclusions:

• The study successfully introduces a new RDoS attack and a robust threat intelligence framework for CoAP in IIoHT.
• Deep learning offers a promising approach for real-time detection and mitigation of advanced cyber threats in healthcare IoT.
• The findings highlight the critical need for enhanced security measures in CoAP implementations within SmartSat-IIoHT environments.