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(to be completed)

 


ThinkMind // SECURWARE 2017, The Eleventh International Conference on Emerging Security Information, Systems and Technologies // View article securware_2017_10_10_30013


Large-Scale Analysis of Domain Blacklists

Authors:
Tran Phuong Thao
Tokunbo Makanju
Jumpei Urakawa
Akira Yamada
Kosuke Murakami
Ayumu Kubota

Keywords: Web Security; Large-Scale Analysis; Empirical Analysis; Blacklist; Malicious Domain

Abstract:
Malicious content has grown along with the explosion of the Internet. Therefore, many organizations construct and maintain blacklists to help web users protect their computers. There are many kinds of blacklists in which domain blacklists are the most popular one. Existing empirical analyses on domain blacklists have several limitations such as using only outdated blacklists, omitting important blacklists, or focusing only on simple aspects of blacklists. In this paper, we analyze the top 14 blacklists including popular and updated blacklists like Safe Browsing from Google and urlblacklist.com. We are the first to filter out the old entries in the blacklists using an enormous dataset of user browsing history. Besides the analysis on theintersections and the registered information from Whois (such as top-level domain, domain age and country), we also build two classification models for web content categories (i.e., education,business, etc.) and malicious categories (i.e., landing and distribution) using machine learning. Our work found some important results. First, the blacklists Safe Browsing version 3 and 4 are being separately deployed and have independent databases with diverse entries although they belong to the same organization. Second, the blacklist dsi.ut-capitole.fr is almosta subset of the blacklist urlblacklist.com with 98% entries. Third, largest portion of entries in the blacklists are created in 2000 with 6.08%, and from United States with 24.28%. Fourth, Safe Browsing version 4 can detect younger domains compared with the others. Fifth, Tech & Computing is the dominant web content category in all the blacklists, and the blacklists in each group (i.e., small public blacklists, large public blacklists, private blacklists) have higher correlation in web content as opposed to blacklists in other groups. Finally, the number of landing domains are larger than that of distribution domains at least 75% in large public blacklists and at least 60% in other blacklists.

Pages: 161 to 167

Copyright: Copyright (c) IARIA, 2017

Publication date: September 10, 2017

Published in: conference

ISSN: 2162-2116

ISBN: 978-1-61208-582-1

Location: Rome, Italy

Dates: from September 10, 2017 to September 14, 2017

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