Classification of Phishing Email Using Random Forest Machine Learning Technique

Table 2

10-Fold cross validation Result.

S/N	Dataset Information			Performance Evaluation
	Email Per Folder	Total Email	P : H Ratio (%)	PA (%)	SR	FP (%)	FN (%)	𝑅 (%)	Pr (%)	𝐹-M (%)	𝑇 (s)
1	15	150	48 : 52	98.00	0.98	0.00	4.11	95.80	100	97.79	11.82
2	30	300	33 : 67	98.33	0.99	0.00	4.00	96.00	100	97.75	21.03
3	50	500	20 : 80	99.20	0.99	0.00	4.00	96.00	100	97.78	33.47
4	100	1000	10 : 90	99.60	0.99	0.00	4.00	96.00	100	97.78	65.46
5	200	2000	10 : 90	99.70	0.99	0.06	2.50	97.50	99.47	98.45	141.25

Key: PA: Prediction Accuracy, SR: Success Rate, FP: False Positive, FN: False Negative, 𝑅: Recall, Pr: Precision, 𝑇: Time, 𝐹-M: 𝐹-Measure, P : H: Phish : Ham.