Authors:
Abdullah Alshehri
;
Frans Coenen
and
Danushka Bollegala
Affiliation:
University of Liverpool, United Kingdom
Keyword(s):
Keystroke Time Series, Continuous Authentication, Keystroke Streams, Behavioral Biometric.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Software Development
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
In this paper, we demonstrate a novel mechanism for continuous authentication of computer users using
keystroke dynamics. The mechanism models keystroke timing features, Flight time (the time between consecutive
keys) and Hold time (the duration of a key press), as a multivariate time series which serves to
dynamically capture typing patterns in real/continuous time. The proposed method differs from previous approaches
for continuous authentication using keystroke dynamics, founded on feature vector representations,
which limited real-time analysis due to the computationally expensive processing of the vectors, and which
also yielded poor authentication accuracy. The proposed mechanism is compared to a feature vector based approach,
taken from the literature, over two datasets. The results indicate superior performance of the proposed
multivariate time series mechanisms for continuous authentication using keystroke dynamics.