Table 7 The evolution of the error rate of the naïve bayesian network related to the records count in the database
From: A novel lightweight URL phishing detection system using SVM and similarity index
Records count | 100 | 250 | 500 | 750 | 1000 | 1250 | 1500 | 1750 | 2000 |
|---|---|---|---|---|---|---|---|---|---|
Naive bayes (%) | 52 | 53.96 | 36.8 | 36.70 | 34.40 | 36.74 | 33.60 | 38.12 | 35.8 |
Naive bayes—JW (%) | 72 | 26.98 | 47.20 | 37.76 | 39.20 | 37.69 | 28.8 | 35.15 | 37.6 |
Naive bayes—H (%) | 16 | 9.52 | 8 | 1.59 | 5.20 | 38.97 | 35.46 | 36.98 | 36 |
Naive bayes—LCS (%) | 20 | 11.11 | 4.80 | 3.19 | 0.4 | 37.06 | 32.80 | 36.75 | 40.6 |
Naive bayes—L (%) | 16 | 19.04 | 2.40 | 2.12 | 4 | 10.22 | 5.86 | 36.75 | 35 |
Naive bayes—NL (%) | 44 | 50.79 | 40.80 | 27.12 | 36.40 | 37.06 | 39.2 | 25.79 | 32.80 |
Naive bayes—QG (%) | 12 | 6.34 | 1.60 | 4.78 | 35.2 | 35.14 | 41.06 | 36.07 | 34.8 |