Multifactor authentication scheme using physically unclonable functions

Fakroon, Moneer; Gebali, Fayez; Mamun, Mohammad Saiful Islam

doi:10.1016/j.iot.2020.100343

Cited by 15 publications

(11 citation statements)

References 17 publications

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“…An authentication and key exchange protocol for smart home IoT system was recently proposed in Fakroon et al (2021), Fakroon et al (2020. The protocol used a multifactor authentication algorithm to preserve user anonymity and untraceability.…”

Section: Related Workmentioning

confidence: 99%

“…In Fakroon et al (2020), the IoT edge devices were assumed to have secret keys stored in NVRAM. Fakroon et al (2021) employed a PUF that gave the IoT edge devices unique identities (IDs). The authors analyzed the proposed schemes through formal analysis using the Burrows-Abadi-Needham logic (BAN), informal analysis and model check using the automated validation of internet security protocols and applications (AVISPA) tool.…”

Section: Related Workmentioning

confidence: 99%

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Review of Physically Unclonable Functions (PUFs): Structures, Models, and Algorithms

Gebali

Mamun

2022

Front. Sens.

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Physically unclonable functions (PUFs) are now an essential component for strengthening the security of Internet of Things (IoT) edge devices. These devices are an important component in many infrastructure systems such as telehealth, commerce, industry, etc. Traditionally these devices are the weakest link in the security of the system since they have limited storage, processing, and energy resources. Furthermore they are located in unsecured environments and could easily be the target of tampering and various types of attacks. We review in this work the structure of most salient types of PUF systems such as static RAM static random access memory (SRAM), ring oscillator (RO), arbiter PUFs, coating PUFs and dynamic RAM dynamic random access memory (DRAM). We discuss statistical models for the five most common types of PUFs and identify the main parameters defining their performance. We review some of the most recent algorithms that can be used to provide stable authentication and secret key generation without having to use helper data or secure sketch algorithms. Finally we provide results showing the performance of these devices and how they depend on the authentication algorithm used and the main system parameters.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Review of Physically Unclonable Functions (PUFs): Structures, Models, and Algorithms

Gebali

Mamun

2022

Front. Sens.

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“…For the client to be authenticated successfully, that response should be valid and the same as what the server expects it to be, as the server already knows the challenge-response pair (CRP) from before [ 31 ].…”

Section: Physical Unclonable Function (Puf)mentioning

confidence: 99%

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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“…It uses physiological or behavioral aspects of humans as a means of authenticating personal identity [3]. Physiological characteristics include those retrieved from the human body, such as iris [4], faces [5,6], retinas, veins [7], fingerprints [8][9][10][11], palm prints, finger knuckle print, and DNA, ECG [12][13][14][15], while behavioral characteristics include voice, stride, signature, and keystroke [16][17][18][19][20]. Biometrics traits are commonly employed in systems such as Security in IoT [21][22][23][24][25], e-banking [26][27][28][29], cloud security [30][31][32][33], access control [34][35][36][37], network security systems [38][39][40][41][42], and ID cards [43][44][45][46][47], and other applications related to IoT [48]…”

Section: Introductionmentioning

confidence: 99%

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

et al. 2022

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Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract deep features from FKP images in this paper. The deep features are collected from the VGG-19 model’s fully connected layer 6 (F6) and fully connected layer 7 (F7). After applying multiple preprocessing steps, such as combining features from different layers and performing dimensionality reduction using principal component analysis (PCA), the extracted deep features are put to the test. The proposed system’s performance is assessed using experiments on the Delhi Finger Knuckle Dataset employing a variety of common classifiers. The best identification result was obtained when the Artificial neural network (ANN) classifier was applied to the principal components of the averaged feature vector of F6 and F7 deep features, with 95% of the data variance preserved. The findings also demonstrate the feasibility of employing these deep features in an FKP recognition system.

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Multifactor authentication scheme using physically unclonable functions

Cited by 15 publications

References 17 publications

Review of Physically Unclonable Functions (PUFs): Structures, Models, and Algorithms

Review of Physically Unclonable Functions (PUFs): Structures, Models, and Algorithms

Multifactor Authentication for Smart Emergency Medical Response Transporters

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

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