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

Cybersecurity

Table 5 Results with Huffman encoding

Model used	A	P	R	F1-Score
RF	0.9870	0.987	0.987	0.987
DT	0.9797	0.980	0.980	0.980
LR	0.7614	0.761	0.761	0.761
SVM	0.8103	0.810	0.810	0.810
NB	0.5247	0.525	0.525	0.525
KNN	0.9038	0.904	0.904	0.904
AdaBoost	0.7717	0.772	0.772	0.772
MLP	0.8911	0.891	0.891	0.891
CNN	0.8129	0.813	0.813	0.813

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. CNN-Convolutional Neural Networks
The highest performance in terms of Accuracy, Precision, Recall, and F1-score are highlighted in bold