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Empirical Study on Malicious URL Detection Using Machine Learning

  • Ripon PatgiriEmail author
  • Hemanth KatariEmail author
  • Ronit KumarEmail author
  • Dheeraj SharmaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11319)

Abstract

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.

Keywords

Malicious URL detection Network security Machine Learning Random Forest Suport vector machine SVM 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National Institute of Technology SilcharSilcharIndia

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