Understanding Insider Attacks in Personalized Picture Password Schemes - IFIP - Lecture Notes in Computer Science Access content directly
Conference Papers Year : 2021

Understanding Insider Attacks in Personalized Picture Password Schemes

Marios Belk
  • Function : Author
  • PersonId : 1028777
Christos Fidas
  • Function : Author
  • PersonId : 1026207
Andreas Pitsillides
  • Function : Author
  • PersonId : 1286569

Abstract

Picture passwords, which require users to complete a picture-based task to login, are increasingly being embraced by researchers as they offer a better tradeoff between security and memorability. Recent works proposed the use of personalized familiar pictures, which are bootstrapped to the users’ prior sociocultural activities and experiences. However, such personalized approaches might entail guessing vulnerabilities by people close to the user (e.g., family members, acquaintances) with whom they share common experiences within the depicted familiar sceneries. To shed light on this aspect, we conducted a controlled in-lab eye-tracking user study (n = 18) focusing on human attack vulnerabilities among people sharing common sociocultural experiences. Results revealed that insider attackers, who share common experiences with the legitimate users, can easily identify regions of their selected secrets. The extra knowledge possessed by people close to the user was also reflected on their visual behavior during the human attack phase. Such findings can drive the design of assistive security mechanisms within personalized picture password schemes.
Fichier principal
Vignette du fichier
520518_1_En_42_Chapter.pdf (494.76 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04215515 , version 1 (22-09-2023)

Licence

Attribution

Identifiers

Cite

Argyris Constantinides, Marios Belk, Christos Fidas, Andreas Pitsillides. Understanding Insider Attacks in Personalized Picture Password Schemes. 18th IFIP Conference on Human-Computer Interaction (INTERACT), Aug 2021, Bari, Italy. pp.722-731, ⟨10.1007/978-3-030-85610-6_42⟩. ⟨hal-04215515⟩
19 View
3 Download

Altmetric

Share

Gmail Facebook X LinkedIn More