Defending touch-based continuous authentication systems from active adversaries using generative adversarial networks

Mehrotra, Pragyan; Shah, Rajiv Ratn (Advisor); Kumar, Rajesh (Advisor)

dc.contributor.author	Mehrotra, Pragyan
dc.contributor.author	Shah, Rajiv Ratn (Advisor)
dc.contributor.author	Kumar, Rajesh (Advisor)
dc.date.accessioned	2023-04-15T14:24:16Z
dc.date.available	2023-04-15T14:24:16Z
dc.date.issued	2021-12
dc.identifier.uri	http://repository.iiitd.edu.in/xmlui/handle/123456789/1195
dc.description.abstract	Previous studies have demonstrated that commonly studied (vanilla) touch-based continuous authentication systems (V-TCAS) are susceptible to population attack. This paper proposes a novel Generative Adversarial Network assisted TCAS (G-TCAS) framework which showed more resilience to the population attack. G-TCAS framework was tested on a dataset of 117 users who interacted with a smartphone and tablet pair. On average, the increase in the false accept rates (FARs) for V-TCAS was much higher (22%) than G-TCAS (13%) for the smartphone. Likewise, the increase in the FARs for V-TCAS was 25% compared to G-TCAS (6%) for the tablet.	en_US
dc.language.iso	en_US	en_US
dc.publisher	IIIT-Delhi	en_US
dc.subject	Soft Biometrics	en_US
dc.subject	Swiping Patterns	en_US
dc.subject	Machine Learning	en_US
dc.subject	Security	en_US
dc.title	Defending touch-based continuous authentication systems from active adversaries using generative adversarial networks	en_US