malicious and benign websites

Abstract: 

One important topic to work is to create a good set of malicious web characteristics, because it is difficult to find one updated and with a research work to support it .

 

This dataset is a another research production of my bachelor students, this is a result of a project that consisted to evaluate classification models to predict malicious and benign websites through their application layer and network characteristics. The data were obtained by a process that included different sources of benign and malicious URL, all of them were verified and used in a low interactive client honeypot in order to get their network traffic, furthermore, we used some tools to get other more information, such as the server country with Whois.

 

This is the first version, but, we have some results of the application of machine learning classifiers in a bachelor thesis and in an article, so, all the data process making and the data description are in above works. But, maybe in the next days I will provide a resume of these in this page.

 

If your papers or other works use our dataset, please cite our paper as follows. Urcuqui, C., Navarro, A., Osorio, J., & Garcıa, M. (2017). Machine Learning Classifiers to Detect Malicious Websites. CEUR Workshop Proceedings. Vol 1950, 14-17.

 

If you need an article of the websites cybersecurity state of the art, you can find it in english and spanish: Urcuqui, C., Peña, M. G., Quintero, J. L. O., & Cadavid, A. N. (2017). Antidefacement. Sistemas & Telemática, 14(39), 9-27.
 

If you have any question or feedback, please do not dude to write at the next email:

ccurcuqui@icesi.edu.co

Instructions: 

One important topic to work is to create a good set of malicious web characteristics, because it is difficult to find one updated and with a research work to support it .

 

This dataset is a another research production of my bachelor students, this is a result of a project that consisted to evaluate classification models to predict malicious and benign websites through their application layer and network characteristics. The data were obtained by a process that included different sources of benign and malicious URL, all of them were verified and used in a low interactive client honeypot in order to get their network traffic, furthermore, we used some tools to get other more information, such as the server country with Whois.

 

This is the first version, but, we have some results of the application of machine learning classifiers in a bachelor thesis and in an article, so, all the data process making and the data description are in above works. But, maybe in the next days I will provide a resume of these in this page.

 

If your papers or other works use our dataset, please cite our paper as follows. Urcuqui, C., Navarro, A., Osorio, J., & Garcıa, M. (2017). Machine Learning Classifiers to Detect Malicious Websites. CEUR Workshop Proceedings. Vol 1950, 14-17.

 

If you need an article of the websites cybersecurity state of the art, you can find it in english and spanish: Urcuqui, C., Peña, M. G., Quintero, J. L. O., & Cadavid, A. N. (2017). Antidefacement. Sistemas & Telemática, 14(39), 9-27.
 

If you have any question or feedback, please do not dude to write at the next email: 

ccurcuqui@icesi.edu.co

Dataset Files

You must be an IEEE Dataport Subscriber to access these files. Subscribe now or login.

Help us make IEEE DataPort better. Sign up to be a Beta Tester and receive a coupon code for a free subscription to IEEE DataPort! Learn More

Dataset Details

Citation Author(s):
Christian Urcuqui, Andrés Navarro, José Osorio, Melisa García
Submitted by:
Christian Urcuqui
Last updated:
Wed, 12/27/2017 - 23:27
DOI:
10.21227/H26Q1T
Data Format:
Links:
 
Cite

Subscribe

[1] Christian Urcuqui, Andrés Navarro, José Osorio, Melisa García, "malicious and benign websites", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H26Q1T. Accessed: Feb. 23, 2018.
@data{h26q1t-17,
doi = {10.21227/H26Q1T},
url = {http://dx.doi.org/10.21227/H26Q1T},
author = {Christian Urcuqui; Andrés Navarro; José Osorio; Melisa García },
publisher = {IEEE Dataport},
title = {malicious and benign websites},
year = {2017} }
TY - DATA
T1 - malicious and benign websites
AU - Christian Urcuqui; Andrés Navarro; José Osorio; Melisa García
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H26Q1T
ER -
Christian Urcuqui, Andrés Navarro, José Osorio, Melisa García. (2017). malicious and benign websites. IEEE Dataport. http://dx.doi.org/10.21227/H26Q1T
Christian Urcuqui, Andrés Navarro, José Osorio, Melisa García, 2017. malicious and benign websites. Available at: http://dx.doi.org/10.21227/H26Q1T.
Christian Urcuqui, Andrés Navarro, José Osorio, Melisa García. (2017). "malicious and benign websites." Web.
1. Christian Urcuqui, Andrés Navarro, José Osorio, Melisa García. malicious and benign websites [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H26Q1T
Christian Urcuqui, Andrés Navarro, José Osorio, Melisa García. "malicious and benign websites." doi: 10.21227/H26Q1T