Abstract
One-Class SVM is an unsupervised algorithm that learns a decision function from only one class for novelty detection: classifying new data as similar (inlier) or different (outlier) to the training set. In this article, we have applied the One-Class SVM to Keystroke Dynamics pattern recognition for user authentication in a remote evaluation system at Laval University. Since all of their students have a short and unique identifier at Laval University, this particular static text is used as the Keystroke Dynamics input for a user to build our own dataset. Then, we were able to identify weaknesses of such a system by evaluating the recognition accuracy depending on the number of signatures and as a function of their number of characters. Finally, we were able to show some correlations between the dispersion and mode of distributions of features characterizing the signatures and the recognition rate.
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Notes
- 1.
IDentifiant Université Laval (IDUL).
- 2.
k-fold Cross-Validation: the training set is split into k smaller sets. For each of the k “folds”, a model is trained using \(k-1\) of the folds as training data, and the resulting model is tested on the remaining part of the data to compute the accuracy. The accuracy reported by k-fold cross-validation is then the average of the values computed in the loop.
- 3.
Accuracy:\(\frac{(TP+TN)}{TP+FP+TN+FN}\), Where TP, TN, FP, FN denotes respectively True Positive, True Negative, False Positive and False Negative.
- 4.
Recall: \(\frac{TP}{(TP+FN)}\) (also called True Positive Rate) measures the proportion of positives that are correctly identified.
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Chang, C., Eude, T., Obando Carbajal, L.E. (2016). Biometric Authentication by Keystroke Dynamics for Remote Evaluation with One-Class Classification. In: Khoury, R., Drummond, C. (eds) Advances in Artificial Intelligence. Canadian AI 2016. Lecture Notes in Computer Science(), vol 9673. Springer, Cham. https://doi.org/10.1007/978-3-319-34111-8_3
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DOI: https://doi.org/10.1007/978-3-319-34111-8_3
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