<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Researchers all over the world have provided significant and effective solutions to detect malicious URLs. Still due to the ever changing nature of cyberattacks, there are many open issues. In this paper, we have provided an effective hybrid methodology with new features to deal with this problem. To evaluate our approach, we have used state-of-the-arts supervised decision tree learning classifications models. We have performed our experiments on the balanced dataset. The experimental results show that, by inclusion of new features all the decision tree learning classifiers work well on our labeled dataset, achieving 98-99% detection accuracy with very low False Positive Rate (FPR) and False Negative Rate (FNR). Also we have achieved 99.29% detection accuracy with very low FPR and FNR using majority voting technique, which is better than the wellknown anti-virus and anti-malware solutions.</p></abstract>

Researchers all over the world have provided significant and effective solutions to detect malicious URLs. Still due to the ever changing nature of cyberattacks, there are many open issues. In this paper, we have provided an effective hybrid methodology with new features to deal with this problem. To evaluate our approach, we have used state-of-the-arts supervised decision tree learning classifications models. We have performed our experiments on the balanced dataset. The experimental results show that, by inclusion of new features all the decision tree learning classifiers work well on our labeled dataset, achieving 98-99% detection accuracy with very low False Positive Rate (FPR) and False Negative Rate (FNR). Also we have achieved 99.29% detection accuracy with very low FPR and FNR using majority voting technique, which is better than the wellknown anti-virus and anti-malware solutions.

Malicious URLs Detection Using Decision Tree Classifiers and Majority Voting Technique

Department of Computer Engineering, R. C. Patel Institute of Technology

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Researchers all over the world have provided significant and effective solutions to detect malicious URLs. Still due to the ever changing nature of...

Malicious URLs Detection Using Decision Tree Classifiers and Majority Voting Technique

Publicado en línea: 30 mar 2018

Páginas: 11 - 29

Recibido: 23 oct 2017

Aceptado: 23 ene 2018

DOI: https://doi.org/10.2478/cait-2018-0002

Palabras clave
Static and dynamic analysis, feature extraction, decision tree learning, malicious URLs, Web security

© 2018 Dharmaraj R. Patil et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Malicious URLs Detection Using Decision Tree Classifiers and Majority Voting Technique

Publicado en línea: 30 mar 2018

Páginas: 11 - 29

Recibido: 23 oct 2017

Aceptado: 23 ene 2018

DOI: https://doi.org/10.2478/cait-2018-0002

Palabras claveStatic and dynamic analysis, feature extraction, decision tree learning, malicious URLs, Web security

© 2018 Dharmaraj R. Patil et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
Static and dynamic analysis, feature extraction, decision tree learning, malicious URLs, Web security