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Towards Federated Learning Intrusion...

Morton, John.

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Towards Federated Learning Intrusion Detection Systems (IDS) Within Internet of Medical Things (IoMT) Ecosystems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Towards Federated Learning Intrusion Detection Systems (IDS) Within Internet of Medical Things (IoMT) Ecosystems./
Author:	Morton, John.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	115 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Information technology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30814398
ISBN:	9798381177763

Towards Federated Learning Intrusion Detection Systems (IDS) Within Internet of Medical Things (IoMT) Ecosystems.
Morton, John.

Towards Federated Learning Intrusion Detection Systems (IDS) Within Internet of Medical Things (IoMT) Ecosystems. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 115 p.

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (D.Engr.)--The George Washington University, 2024.

This item must not be sold to any third party vendors.

This research demonstrates how federated learning (FL), and architecture could be leveraged across Internet of Medical Things (IoMT) ecosystems with Intrusion detection systems detect and mitigate cyber threats. The IoMT is comprised of heterogenous devices such as wearable, implants and on-person sensors that transmit and receive medical data. These devices are vulnerable to diverse types of security threats, and thus present a significant risk to a patient's privacy and safety. Because security is a critical factor for successfully merging IoMT into pervasive healthcare systems, there is an urgent need for new security mechanisms to prevent threats on the IoMT edge network.Federated learning (FL) provides enhanced privacy as compared to traditional centralized learning models. This is extremely important in healthcare ecosystems to provide protection on Personally Identifiable Information (PII) and Personal Health Information (PHI) data which cannot be delivered with traditional centralized methods. Additionally, implementing decentralized solutions provides enhanced optimization techniques not prevalent in centralized learning traditional methods. (R. Durga, 2021) The integration of blockchain provides a fully decentralized ecosystem aimed to provide resiliency and immutable protections on personal data. The research analyzes cyber threat detection using centralized and federated learning models. Fog nodes are employed with IoMT gateways to minimize data in transit and accelerate detection of cyber threats which may impact local IoMT devices. Hyperledger Fabric Blockchain is used to mitigate the FL trust and security shortcomings. (Sun J, 2022) Due to the limited publicly available healthcare datasets, this research will explore analysis on WUSTL-EHMS-2020 dataset to provide an enriched solution comprised of biometric and network attack data. The rationale for using this dataset is that IoMT is a subset of the broader IoT ecosystem. (Manickam P, 2022) Several threat vectors associated with IoT are prevalent within healthcare IoMT. Our focus is to identify cyber threats within the healthcare network that provide services consumed by providers for patient diagnosis. Leveraging machine learning techniques, the research aims to provide models that are equivalent or better efficacy as compared to centralized techniques. Enhancing privacy is an important characteristic of the healthcare domain.The results will aim to demonstrate the feasibility of using Federated Learning (FL) as compared to CL methods supported by blockchain to enhance privacy IoMT based systems which have reduced computational and network resources than a traditional centralized approach.

ISBN: 9798381177763Subjects--Topical Terms:

532993
Information technology.
Subjects--Index Terms:

Cybersecurity

Towards Federated Learning Intrusion Detection Systems (IDS) Within Internet of Medical Things (IoMT) Ecosystems.
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520 $a This research demonstrates how federated learning (FL), and architecture could be leveraged across Internet of Medical Things (IoMT) ecosystems with Intrusion detection systems detect and mitigate cyber threats. The IoMT is comprised of heterogenous devices such as wearable, implants and on-person sensors that transmit and receive medical data. These devices are vulnerable to diverse types of security threats, and thus present a significant risk to a patient's privacy and safety. Because security is a critical factor for successfully merging IoMT into pervasive healthcare systems, there is an urgent need for new security mechanisms to prevent threats on the IoMT edge network.Federated learning (FL) provides enhanced privacy as compared to traditional centralized learning models. This is extremely important in healthcare ecosystems to provide protection on Personally Identifiable Information (PII) and Personal Health Information (PHI) data which cannot be delivered with traditional centralized methods. Additionally, implementing decentralized solutions provides enhanced optimization techniques not prevalent in centralized learning traditional methods. (R. Durga, 2021) The integration of blockchain provides a fully decentralized ecosystem aimed to provide resiliency and immutable protections on personal data. The research analyzes cyber threat detection using centralized and federated learning models. Fog nodes are employed with IoMT gateways to minimize data in transit and accelerate detection of cyber threats which may impact local IoMT devices. Hyperledger Fabric Blockchain is used to mitigate the FL trust and security shortcomings. (Sun J, 2022) Due to the limited publicly available healthcare datasets, this research will explore analysis on WUSTL-EHMS-2020 dataset to provide an enriched solution comprised of biometric and network attack data. The rationale for using this dataset is that IoMT is a subset of the broader IoT ecosystem. (Manickam P, 2022) Several threat vectors associated with IoT are prevalent within healthcare IoMT. Our focus is to identify cyber threats within the healthcare network that provide services consumed by providers for patient diagnosis. Leveraging machine learning techniques, the research aims to provide models that are equivalent or better efficacy as compared to centralized techniques. Enhancing privacy is an important characteristic of the healthcare domain.The results will aim to demonstrate the feasibility of using Federated Learning (FL) as compared to CL methods supported by blockchain to enhance privacy IoMT based systems which have reduced computational and network resources than a traditional centralized approach.
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653 $a Medical data
653 $a Personal Health Information
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