Authors:
Paulo Henrique Pisani
and
Silvio do Lago Pereira
Affiliation:
FATEC-SP/CEETEPS, Brazil
Keyword(s):
User profiling, Keystroke dynamics, Evolutionary artificial neural networks, Hybrid training, Lamarckian evolution.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolution Strategies
;
Evolutionary Computing
;
Evolutionary Multiobjective Optimization
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
The pace of computing and communications development has contributed to an increased data exposure and, consequently, to the rise of an issue known as identity theft. By applying user profiling, which analyzes the user behavior in order to perform a continuous authentication, protection of digital identities can be enhanced. Among the possible features to be analyzed, this paper focuses on keystroke dynamics, something that cannot be easily stolen. As keystroke dynamics involves dealing with noisy data, it was chosen a neural network to perform the pattern recognition task. However, traditional neural network training algorithms are bound to get trapped in local minimum, reducing the learning ability. This work draws a comparison between backpropagation and two hybrid approaches based on evolutionary training, for the task of keystroke dynamics. Differently from most evolutionary algorithms based on Darwinism, this work also studies Lamarckian evolutionary algorithms that, although n
ot being biologically plausible, attained promising results in the tests.
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