Table 4 Classifiers used in keystroke dynamics

Montalvao et al. [37]

Bleha [4]

Monrose and Rubin [36]

Gunetti and Picardi [22]

Giot et al. [17]

SVM

Statistical

Neural network

Classifier based on distance

Giot et al. [19]

SVM

Statistical

Classifier based on Euclidean distance

Classifier based on Hamming distance

Killourhy and Maxion [30]

Nearest neighbour

Neural network

Mean-based classifier

Rodrigues et al. [43]

Hidden Markov Model (HMM)

Statistical

Hosseinzadeh and Krishnan [24]

Gaussian Mixture Model (GMM) + Leave one out method

Killourhy and Maxion [31]

Nearest neighbour

Outlier count (z-score)

Manhattan distance

Bartlow and Cukic [3]

Random Forests

Chang [9]

Tree-based with Euclidean distance

Montalvao Filho and Freire [14]

Bleha [4]

Monrose and Rubin [36]

1D-Histogram and 2D-Histogram

Gunetti and Piccardi [22]

Proposed Methods: R Measure and A Measure

Monrose and Rubin [36]

Euclidean distance

Weighted and non-weighted probability

Bayes

Yu e Cho [48]

SVM \(^1\)

2-layer and 4-layer Auto Associative Multi-layer Perceptron (AAMLP)

Giot et al. [20]

Based on Gaussian distribution [23]

Chang et al. [8]

Statistical [5]

Killourhy and Maxion [32]

Statistical

Disorder-based