Weak PassPoint Passwords Detected by the Perimeter of Delaunay Triangles

Lisset Suárez-Plasencia; Carlos Miguel Legón-Pérez; Joaquín Alberto Herrera-Macías; Raisa Socorro-Llanes; Rojas, Omar; Sosa-Gómez, Guillermo

doi:10.1155/2022/3624587

Weak PassPoint Passwords Detected by the Perimeter of Delaunay Triangles

Journal

Security and Communication Networks

ISSN

1939-0122

1939-0114

Date Issued

2022

Author(s)

Lisset Suárez-Plasencia

Carlos Miguel Legón-Pérez

Joaquín Alberto Herrera-Macías

Raisa Socorro-Llanes

Rojas, Omar

Escuela Superior de Dirección y Administración de Instituciones - CampGDL

Sosa-Gómez, Guillermo

Facultad de Ingeniería - CampGDL

Type

text::journal::journal article

DOI

10.1155/2022/3624587

URL

https://scripta.up.edu.mx/handle/20.500.12552/4195

Abstract

PassPoint is a graphical authentication technique that is based on the selection of five points in an image. A detected vulnerability lies in the possible existence of a pattern in the points that make up the password. The objective of this work is to detect nonrandom graphical passwords in the PassPoint scenario. A spatial randomness test based on the average of Delaunay triangles’ perimeter is proposed, given the ineffectiveness of the classic tests in this scenario, which only consists of five points. A state-of-the-art of various applications of Voronoi polygons and Delaunay triangulations are presented to detect clustered and regular patterns. The distributions of the averages of the triangles’ perimeters in the PassPoint scenario for various sizes of images are disclosed, which were unknown. The test’s decision criterion was constructed from one of the best distributions to which the data were adjusted. Type I and type II errors were estimated, and it was concluded that the proposed test could detect clustered and regular graphical passwords in PassPoint, therefore being more effective in detecting clustering than regularity.