Table 5.

Comparison table of state-of-the-art studies focusing on phishing techniques

Authors	Classification	Feature selection technique	Accuracy
James et al. [36]	J48, IBK, SVM, NB	–	89.75%
Subasi et al. [57]	ANN, kNN, RF, SVM, C4.5, RF	–	97.36%
Abdelhamid et al. [9]	eDRI	–	93.5%
Mao et al. [44]	SVM, DT	–	93%
Jain and Gupta [34]	–	–	99.09%
Yao et al. [63]	–	–	98.3%
Patil et al. [53]	LR, DT, RF	–	96.58%
Jagadeesan et al. [33]	RF, SVM	–	95.11%
Hota et al. [29]	CART, C4.5	RRFST	99.11%
Tyagi et al. [58]	DT, RF, GBM	PCA	98.40%
Curtis et al. [21]	–	–	–
Sahingoz et al. [54]	SVM, DT, RF, kNN, KS, NB	NLP	97.98%
Parsons et al. [52]	–	–	–
Joshi et al. [39]	RF, RA	RA	97.63%
Ubing et al. [59]	EL	–	95.4%
Mao et al. [45]	SVM, RF, DT, AB	–	97.31%
Williams et al. [62]	–	–	–
Niranjan et al. [48]	RC, kNN, IBK, LR, PART	–	97.3%
Chen and Chen [17]	ELM, SVM, LR, C4.5, LC-ELM, kNN, XGB	ANOVA	99.2%
Chiew et al. [19]	RF, C4.5, PART, SVM, NB	–	96.17%
Pandey et al. [50]	SVM, RF	–	94%