SRAM Physically Unclonable Functions for Smart Home IoT Telehealth Environments

Gebali, Fayez; Mamun, Mohammad Saiful Islam

doi:10.1002/9781119987451.ch3

Cited by 7 publications

(7 citation statements)

References 20 publications

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“…Authenticating IoT devices also is challenging and requires special techniques. For example, Physical Unclonable Function (PUF) is used to authenticate the hardware for the telehealth distributed devices [20].…”

Section: Access Control Enginementioning

confidence: 99%

Zero Trust Context-Aware Access Control Framework for IoT Devices in Healthcare Cloud AI Ecosystem

Al-hammuri,

Gebali,

Kanan

et al. 2023

Preprint

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It is essential for modern healthcare systems to utilize the Internet of Things (IoT) devices that facilitate and establish the infrastructure for smart hospitals and telehealth. The advancement in telehealth technology and the increasing penetration of IoT devices make them vulnerable to different types of attacks, which require additional research and development for security tools. This article proposes a zero trust context-aware framework to manage the access of the main components in the cloud ecosystem, the users, IoT devices and output data. The framework also considers regulatory compliance and maintains the chain of trust by proposing a critical and bond trust scoring assessment that is based on a set of features and cloud-native micro-services, including authentication, encryption, logging, authorizations and machine learning like the word2vec model within Cloud AI ecosystem.

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Section: Access Control Enginementioning

confidence: 99%

Zero Trust Context-Aware Access Control Framework for IoT Devices in Healthcare Cloud AI Ecosystem

Al-hammuri,

Gebali,

Kanan

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In a telehealth environment, where humans (e.g., doctors) and CPS (e.g., Internet of Medical Things (IoMT) devices) work together to accomplish a task (e.g., operating on a patient), the nature of the relationship can be addressed as an interaction between two systems: the human (biological) and medical devices (cyber-physical) [ 23 ]. Physically unclonable functions (PUFs) is the preferred option for endowing hardware components with unique identities [ 24 ]. Figure 2 demonstrates the available entities in a telehealth system and the overall schema that can be considered for authentication.…”

Section: Authenticationmentioning

confidence: 99%

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Fard

Gebali

Mamun

2022

Sensors

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Telehealth systems have evolved into more prevalent services that can serve people in remote locations and at their homes via smart devices and 5G systems. Protecting the privacy and security of users is crucial in such online systems. Although there are many protocols to provide security through strong authentication systems, sophisticated IoT attacks are becoming more prevalent. Using machine learning to handle biometric information or physical layer features is key to addressing authentication problems for human and IoT devices, respectively. This tutorial discusses machine learning applications to propose robust authentication protocols. Since machine learning methods are trained based on hidden concepts in biometric and physical layer data, these dynamic authentication models can be more reliable than traditional methods. The main advantage of these methods is that the behavioral traits of humans and devices are tough to counterfeit. Furthermore, machine learning facilitates continuous and context-aware authentication.

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“…International Journal of Telemedicine and Applications processing [30,38,39]. This approach selects lownoise bits of the response bits such that encoding the PUF response generates noise-free data 3.2.…”

Section: Physical Unclonable Function (Puf)mentioning

confidence: 99%

“…Eliminating the dynamic noise can be accomplished using two approaches: Using forward error correction (FEC) such as the helper data algorithm [ 32 – 34 ] or fuzzy extractors [ 35 – 37 ] Using statistical properties of the PUF response to generate a reliable response without doing any processing [ 30 , 38 , 39 ]. This approach selects low-noise bits of the response bits such that encoding the PUF response generates noise-free data …”

Section: Physical Unclonable Function (Puf)mentioning

confidence: 99%

See 1 more Smart Citation

Multifactor Authentication for Smart Emergency Medical Response Transporters

Alghamdi

Gebali

Salem

2022

International Journal of Telemedicine and Applications

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Securing telehealth IoT infrastructure is essential to provide high-level medical care and prevent cyberattacks. A vulnerable stage in IoT telehealth is while the patient is being transported to a healthcare facility, the transporter could be an ambulance or an air ambulance. In this paper, we propose a multifactor authentication scheme to secure the system when the patient is in transit to the healthcare facility. We apply this scheme to an ambulance, using physical unclonable functions (PUFs) embedded in the ambulance to facilitate authentication and secure key exchange. We validated the security of the proposed scheme using formal and informal security analysis. The analysis supports our claim that the proposed scheme protects against many types of attacks.

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SRAM Physically Unclonable Functions for Smart Home IoT Telehealth Environments

Cited by 7 publications

References 20 publications

Zero Trust Context-Aware Access Control Framework for IoT Devices in Healthcare Cloud AI Ecosystem

Zero Trust Context-Aware Access Control Framework for IoT Devices in Healthcare Cloud AI Ecosystem

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Multifactor Authentication for Smart Emergency Medical Response Transporters

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