GA-SVM wrapper approach for feature subset selection in keystroke dynamics identity verification

Abstract

Password is the most widely used identity verification method in computer security domain. However, due to its simplicity, it is vulnerable to imposter attacks. Keystroke dynamics adds a shield to password. Password typing patterns or timing vectors of a user are measured and used to train a novelty detector model. However, without manual pre-processing to remove noises and outliers resulting from typing inconsistencies, a poor detection accuracy results. Thus, in this paper, we propose an automatic feature subset selection process that can automatically selects a relevant subset of features and ignores the rest, thus producing a better accuracy. Genetic algorithm is employed to implement a randomized search and SVM, an excellent novelty detector with fast learning speed, is employed as a base learner. Preliminary experiments show a promising result.