Cybersecurity

Table 6 Results with Logarithmic transformation

From: Android malware category detection using a novel feature vector-based machine learning model

Model used	A	P	R	F1-Score
RF	0.9306	0.931	0.931	0.931
DT	0.8897	0.890	0.890	0.890
LR	0.8586	0.859	0.859	0.859
SVM	0.8797	0.880	0.880	0.880
NB	0.4715	0.472	0.472	0.472
KNN	0.8926	0.893	0.893	0.893
AdaBoost	0.7862	0.786	0.786	0.786
MLP	0.9254	0.925	0.925	0.925
CNN	0.7762	0.776	0.776	0.776

A-Accuracy, P-Precision, R-Recall, RF-Random Forest, DT-Decision Tree, LR-Logistic Regression, SVM-Support Vector Machine, NB-Naïve-Bayes, KNN-K Nearest Neighbour, MLP-Multi-Layer Perceptron
The highest performance in terms of Accuracy, Precision, Recall, and F1-score are highlighted in bold

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