A Semisupervised Majority Weighted Vote Antiphishing Attacks IDS for the Education Industry

Table 1

Performance measures.

Model	Accuracy	Auc	Recall	Prec.	F1	Kappa	MCC	TT (sec)
Voting naive bayes positive	0.9314	0.9982	0.9292	0.9320	0.9312	0.8722	0.8871	2.339
Light gradient boosting machine	0.8949	0.9777	0.8770	0.8970	0.8941	0.8197	0.8218	0.244
Extreme gradient boosting	0.8942	0.9759	0.8745	0.8976	0.8935	0.8187	0.8211	15.896
CatBoost classifier	0.8926	0.9763	0.8710	0.8950	0.8921	0.8154	0.8172	4.328
Random forest classifier	0.8918	0.9739	0.8685	0.8961	0.8918	0.8145	0.8169	0.562
Gradient boosting classifier	0.8864	0.9747	0.8635	0.8914	0.8861	0.8053	0.8082	0.665
SVM - radial kernel	0.8726	0.9498	0.8388	0.8765	0.8716	0.7806	0.7832	0.387
k-Neighbors classifier	0.8687	0.9494	0.8336	0.8700	0.8666	0.7727	0.7753	0.128
MLP classifier	0.7988	0.8728	0.8076	0.7877	0.7541	0.7719	0.7056	6.322