Empirical Study on Malicious URL Detection Using Machine Learning

In this paper, the malicious URLs detection is treated as a binary classification problem and performance of several well-known classifiers are tested with test data. The algorithms Random Forests and support Vector Machine (SVM) are studied in particular which attain a high accuracy. These algorithms are used for training the dataset for classification of good and bad URLs. The dataset of URLs is divided into training and test data in 60:40, 70:30 and 80:20 ratios. Accuracy of Random Forests and SVMs is calculated for several iterations for each split ratio. According to the results, the split ratio 80:20 is observed as more accurate split and average accuracy of Random Forests is more than SVMs. SVM is observed to be more fluctuating than Random Forests in accuracy.