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  3. Secure And Enhanced Cyber-threat Detection In Iomt Using Locally Deployed Large Language Models.
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  3. Secure And Enhanced Cyber-threat Detection In Iomt Using Locally Deployed Large Language Models.

Related Experiment Video

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Secure and Enhanced Cyber-Threat Detection in IoMT Using Locally Deployed Large Language Models.

Saadullah Farooq Abbasi1, Muhammad Bilal2, Xuefei Ding1

  • 1Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, United Kingdom.

Studies in Health Technology and Informatics
|May 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a locally deployed Llama 3.1 large language model (LLM) for enhanced cybersecurity in the Internet of Medical Things (IoMT). It identifies more cyberthreats than existing tools while protecting patient privacy.

Keywords:
Information SecurityInternet of Medical ThingsLarge Language ModelsThreat Modeling

Related Experiment Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

  • Cybersecurity
  • Health Informatics
  • Artificial Intelligence

Background:

  • The Internet of Medical Things (IoMT) is rapidly expanding, integrating advanced technologies like image processing and large language models (LLMs).
  • Processing patient-identifiable information (PII) in cloud environments presents significant security and compliance challenges.
  • Current threat detection tools offer basic protection but fail to identify complex vulnerabilities, and cloud-based LLMs compromise data privacy.

Purpose of the Study:

  • To propose a novel, locally deployed, and cost-efficient LLM solution for enhancing IoMT cybersecurity.
  • To improve the detection of complex cyberthreats and provide mitigation strategies.
  • To address the privacy concerns associated with cloud-based LLM approaches.

Main Methods:

  • Implementation of a locally deployed Llama 3.1 LLM (8B parameters) for cybersecurity threat analysis.
  • Comparison of the local LLM's detection capabilities against existing tools like SPYDERISK.
  • Evaluation of the model's effectiveness in identifying additional cyberthreats and recommending mitigation strategies.

Main Results:

  • The locally deployed Llama 3.1 model successfully identified 12 additional cyberthreats beyond those detected by SPYDERISK.
  • The enhanced detection coverage significantly improved the overall security posture for IoMT environments.
  • The privacy-preserving nature of local deployment was maintained throughout the threat detection process.

Conclusions:

  • Local deployment of Llama 3.1 offers a superior and privacy-preserving method for IoMT cybersecurity.
  • This approach effectively addresses the limitations of existing tools and cloud-based LLM solutions.
  • The study demonstrates the potential of localized LLMs for robust threat identification and mitigation in sensitive healthcare settings.