Authentication and Authorization for Mobile IoT Devices Using Biofeatures: Recent Advances and Future Trends

Table 2

Machine learning and data mining methods used by authentication and authorization schemes for mobile IoT devices.

Machine learning and data mining methods	Schemes	EER	Accuracy	FAR	FRR
Agglomerative complete link clustering approach	[22]	19.68%	n/a	n/a	n/a
Support vector distribution estimation	[23]	0.52%	n/a	n/a	n/a
	[24]	0 - 4%	n/a	n/a	n/a
Gaussian mixture model	[25]	2.13%	n/a	n/a	n/a
k-nearest-neighbors (kNN)	[24]	0% - 4%	n/a	n/a	n/a
	[26]	n/a	87.8%	18.3%	6.1%
	[27]	n/a	n/a	0.37%	1.12%
	[28]	n/a	96.4%	3.6%	0%
	[29]	n/a	96.86%	n/a	n/a
	[30]	3.7%	n/a	n/a	n/a
	[31]	0.5%	n/a	n/a	n/a
Support vector machine (SVM)	[24]	0 - 4%	n/a	n/a	n/a
	[32]	7.16%	n/a	n/a	n/a
	[33]	n/a	96.0%	n/a	n/a
	[34]	n/a	n/a	0.023%	0.044%
	[35]	n/a	n/a	2.10%	2.24%
	[36]	n/a	n/a	0.004%	0.01%
	[26]	n/a	87.8%	18.3%	6.1%
	[27]	n/a	n/a	0.37%	1.12%
	[37]	1.3%	n/a	2.96%	0.86%
	[35]	n/a	n/a	2.61%	2.51%
	[29]	n/a	98%	n/a	n/a
A computation efficient statistical classifier	[38]	10.00%	n/a	9.78%	10.00%
Deep learning	[39]	0.02%	n/a	n/a	n/a
	[40]	n/a	99.58%	n/a	n/a
	[41]	n/a	98.55-99.71%	n/a	n/a
	[42]	n/a	99.10%	n/a	n/a
	[43]	n/a	97.5%	n/a	n/a
Local binary patterns algorithm	[44]	0.1-0.13%	n/a	n/a	n/a
Mel-frequency cepstral coefficients	[45]	n/a	80.6%	0.01%	15%
Pupillary light reflex	[46]	11.37%	n/a	n/a	n/a
Euclidean distance, hamming distance	[47]	n/a	0.9992%	0%	0.0015%
Deep convolutional neural network	[33]	n/a	96.0%	n/a	n/a
	[48]	n/a	n/a	1.5%	n/a
	[49]	8.6%	91.4	n/a	n/a
	[50]	n/a	93.2	n/a	n/a
	[51]	3.1%	n/a	n/a	n/a
Genetic algorithm	[52]	0.46%	n/a	n/a	n/a
Artificial neural network (ANN)	[53]	2.13%	n/a	n/a	n/a
	[54]	2.46%	n/a	n/a	n/a
Gauss-Newton based neural network	[55]	4.1%	n/a	3.33%	3.33%
Radial integration transform	[56]	10.8%	n/a	n/a	n/a
Weibull distribution	[57]	2-10%	n/a	n/a	n/a
Online learning algorithms	[58]	0.04%	96%	n/a	n/a
Random forest (RF)	[59]	7.5%	n/a	17.66%	n/a
Neural network (NN)	[27]	n/a	n/a	0.37%	1.12%
	[28]	n/a	96.4%	3.6%	0%
	[60]	n/a	n/a	15%	0%
Circular integration transform	[56]	10.8%	n/a	n/a	n/a
Decision tree (DT)	[26]	n/a	86.4%	16.1%	11.0%
	[35]	n/a	n/a	2.10%	2.24%
	[61]	n/a	n/a	0.88%	9.62%
	[62]	n/a	n/a	0.005%	3.027%
	[29]	n/a	91.72%	n/a	n/a
Learning Algorithm for Multivariate Data Analysis (LAMDA)	[63]	n/a	n/a	0%	0.36%
Bayesian network (BN)	[35]	n/a	n/a	2.47%	2.53%
	[29]	n/a	95.02%	n/a	n/a
Naive Bayes	[29]	n/a	93.7%	n/a	n/a
	[36]	n/a	n/a	0.004%	0.01%
	[64]	8.21%	n/a	n/a	n/a
Pearson product-moment correlation coefficient (PPMCC)	[28]	n/a	96.4%	3.6%	0%
Keyed random projections and arithmetic hashing	[65]	7.28%	n/a	n/a	n/a
One-dimensional multiresolution local binary patterns	[66]	7.89%	n/a	1.57%	0.39%

EER: equal error rate; FAR: false acceptance rate, FRR: false rejection rate; n/a: not available.