Machine Learning Algorithms In User Authentication Schemes

Pryor, Laura; Dave, Rushit; Seliya, Naeem; Boone, Evelyn R Sowells

doi:10.1109/icecet52533.2021.9698440

Cited by 15 publications

(5 citation statements)

References 23 publications

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“…Moreover, Shahbazi et al [94] used DRL in the context of blockchain to authenticate IoT devices in smart environment gateway, such as smart healthcare. Clustering [71,72,73,74] Behavioral Biometrics [78] Classification [80,81,82] k-NN [4,57] PCA [75,76] Outlier Detection [79] Feature Selection [38] RF [4,58,59,60,61,62,63,64] SVD [77] -Adaptive Authentication [80] SVM [4,65,66,67,68] --CAN [81,82]…”

Section: Deep Reinforcement Learningmentioning

confidence: 99%

“…Authentication provides access control for systems by controlling whether a user's credentials align with the available records on the server. Traditional security controls rely on static authentication methods [4], such as passwords, login patterns, or personal www.videleaf.com identification numbers (PINs). Besides the simplicity and accessibility of these methods, they are vulnerable to impersonation [5].…”

Section: Introductionmentioning

confidence: 99%

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Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Hazratifard,

Gebali,

Mamun

2023

Prime Archives in Sensors

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Section: Deep Reinforcement Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Hazratifard,

Gebali,

Mamun

2023

Prime Archives in Sensors

View full text Add to dashboard Cite

show abstract

“…Naïve Bayes (NB) [ 52 ], k-Nearest Neighbor (k-NN) [ 52 ], random forest (RF) [ 53 ], and support vector machine (SVM) [ 52 ] are popular supervised ML methods that have been widely used in the literature to address authentication problems [ 4 ].…”

Section: Machine Learning Models In Authentication Schemes Of Telehealthmentioning

confidence: 99%

“…Authentication provides access control for systems by controlling whether a user’s credentials align with the available records on the server. Traditional security controls rely on static authentication methods [ 4 ], such as passwords, login patterns, or personal identification numbers (PINs). Besides the simplicity and accessibility of these methods, they are vulnerable to impersonation [ 5 ].…”

Section: Introductionmentioning

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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“…The significant disparity in growth trajectories observed between devices and their corresponding security measures increasingly exposes a heightened vulnerability, wherein an expanding number of devices are susceptible to infiltration by malicious actors. Building upon prior research in the domain, Pryor et al [5] investigated several ML methods employed in user authentication systems that incorporate touch dynamics and device mobility. The objective of this paper was to provide a complete examination of the present applications of various ML algorithms commonly employed in user authentication systems that incorporate touch dynamics and device movement.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Work to Highlight the Superiority of Mouth Brooding Fish (MBF) over the Various ML Techniques in Password Security Classification

Shi,

Wang

2024

ijacsa

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Within the domain of password security classification, the pursuit of practical and dependable methodologies has prompted the examination of both biological and technological paradigms. The present study investigates the efficacy of Mouth Brooding Fish (MBF) as an innovative method in contrast to conventional Machine Learning (ML) approaches for classifying password security. The research approach entails a rigorous examination of the comparative analysis of MBF and ML algorithms, evaluating their effectiveness in password classification using many criteria, including accuracy, robustness, flexibility, and durability against adversarial assaults. The findings suggest that ML approaches have shown significant effectiveness in classifying passwords. However, using methodologies inspired by the minimum Bayes risk framework demonstrates a higher degree of resistance against typical cyber dangers. The intrinsic biological mechanisms of MBF, encompassing adaptive behaviors and inherent protection, play a role in enhancing the resilience and adaptability of the password security categorization system. The results offer significant insights that can inform the evolution of password security systems, integrating biological principles with technical progress to enhance safeguarding measures in digital environments. To emphasize the advantages of the suggested approach, several ML approaches are investigated, such as Support Vector Machines (SVM), AdaBoost, Multilayer Perceptron (MLP), Gaussian Kernel (GK), and Random Forest (RF). The F-score, accuracy, sensitivity, and specificity metrics for MBF exhibit noteworthy performance compared to the other selected models, with values of 100%.

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Machine Learning Algorithms In User Authentication Schemes

Cited by 15 publications

References 23 publications

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

A Comparative Work to Highlight the Superiority of Mouth Brooding Fish (MBF) over the Various ML Techniques in Password Security Classification

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