Auto Semi-supervised Outlier Detection for Malicious Authentication Events

Kaiafas, Georgios; Hammerschmidt, Christian; Lagraa, Sofiane; State, Radu

doi:10.1007/978-3-030-43887-6_14

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Deep Reinforcement Learning1

Machine Learning Models In Authentication Schemes Of Telehealth1

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2021

2023

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Cited by 3 publications

(2 citation statements)

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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%

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%

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Hazratifard,

Gebali,

Mamun

2023

Prime Archives in Sensors

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“…This behavioral authentication model is very applicable to authenticate legal operators of telehealth platforms while connecting with patients or accessing their data. Moreover, Kaiafas et al [ 79 ] employed a semi-supervised outlier detection method for authentication.…”

Section: Machine Learning Models In Authentication Schemes Of Telehealthmentioning

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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