The House That Knows You

Ongun, Talha; Oprea, Alina; Nita-Rotaru, Cristina; Christodorescu, Mihai; Salajegheh, Negin

doi:10.1145/3243734.3278523

Cited by 9 publications

(3 citation statements)

References 3 publications

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“…With technological advances, biometrics is used as a tool to identify people and collect information about their Peris-Lopez, 2016), the internet of things (Obaidat, Traore, & Woungang, 2019;Duraibi, 2020;Ongun et al, 2018), and health care (Mohsin et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

A Review of Recent Machine Learning Approaches for Voice Authentication Systems

CAN¹,

Atilgan²

2023

Bilgi Ve İletişim Teknolojileri Dergisi

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Voice authentication systems are a comfortable way of protection since users do not need to remember passwords or carry identification cards. As a unique identifier for all individuals, voice is a practical tool to authenticate people into security services. In this paper, recent single-modal and multimodal voice authentication studies are reviewed with an explanation of underlying feature extraction and classification methods. This paper also discusses security attacks on voice authentication systems, including random attacks, mimicry attacks, replay attacks, voice synthesizing attacks, counterfeit attacks, and hidden voice command attacks.

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Section: Discussionmentioning

confidence: 99%

A Review of Recent Machine Learning Approaches for Voice Authentication Systems

CAN¹,

Atilgan²

2023

Bilgi Ve İletişim Teknolojileri Dergisi

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“…Shi et al [ 55 ] developed a deep learning user authentication scheme in university office and apartment environments, and could achieve over 94% authentication accuracy for 11 real users based on WiFi signals. Ongun et al [ 38 ] collected data from 15 IoT devices in a lab to classify 6 real users with 86% accuracy, and 5 real users with 97% accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Another work used total response time metric in order to evaluate usability in financial transaction scenarios [ 36 ], and a study regarding real-time inference in edge devices evaluated response time to consider user perspective advantages of the proposed approach [ 37 ]. An exploratory study regarding user recognition based on Alexa Skills usage is presented in [ 38 ].…”

Section: Related Workmentioning

confidence: 99%

Non-Invasive Challenge Response Authentication for Voice Transactions with Smart Home Behavior

Hayashi

Ruggiero

2020

Sensors

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Smart speakers, such as Alexa and Google Home, support daily activities in smart home environments. Even though voice commands enable friction-less interactions, existing financial transaction authorization mechanisms hinder usability. A non-invasive authorization by leveraging presence and light sensors’ data is proposed in order to replace invasive procedure through smartphone notification. The Coloured Petri Net model was created for synthetic data generation, and one month data were collected in test bed with real users. Random Forest machine learning models were used for smart home behavior information retrieval. The LSTM prediction model was evaluated while using test bed data, and an open dataset from CASAS. The proposed authorization mechanism is based on Physical Unclonable Function usage as a random number generator seed in a Challenge Response protocol. The simulations indicate that the proposed scheme with specialized autonomous device could halve the total response time for low value financial transactions triggered by voice, from 7.3 to 3.5 s in a non-invasive manner, maintaining authorization security.

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