Research Article
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 (%) | (%) | Pr (%) | -M (%) | (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 |
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Key: PA: Prediction Accuracy, SR: Success Rate, FP: False Positive, FN: False Negative, : Recall, Pr: Precision, : Time, -M: -Measure, P : H: Phish : Ham.
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