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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23181

Title: Detecting Malicious URLs using Machine Learning Techniques
Authors: Vanhoenshoven, Frank
Nápoles Ruiz, Gonzalo
Falcon, Rafaël
Vanhoof, Koen
Koeppen, Mario
Issue Date: 2016
Publisher: IEEE eXpress Conference Publishing
Citation: Proceedings of 2016 IEEE symposium series on computational intelligence (SSCI)
Abstract: The World Wide Web supports a wide range of criminal activities such as spam-advertised e-commerce, financial fraud and malware dissemination. Although the precise motivations behind these schemes may differ, the common denominator lies in the fact that unsuspecting users visit their sites. These visits can be driven by email, web search results or links from other web pages. In all cases, however, the user is required to take some action, such as clicking on a desired Uniform Resource Locator (URL). In order to identify these malicious sites, the web security community has developed blacklisting services. These blacklists are in turn constructed by an array of techniques including manual reporting, honeypots, and web crawlers combined with site analysis heuristics. Inevitably, many malicious sites are not blacklisted — either because they are too recent or were never or incorrectly evaluated. In this paper, we address the detection of malicious URLs as a binary classification problem and study the performance of several well-known classifiers, namely Na\"ive Bayes, Support Vector Machines, Multi-Layer Perceptron, Decision Trees, Random Forest and k-Nearest Neighbors. Furthermore, we adopted a public dataset comprising 2.4 million URLs (instances) and 3.2 million features. The numerical simulations have shown that most classification methods achieve acceptable prediction rates without requiring either advanced feature selection techniques or the involvement of a domain expert. In particular, Random Forest and Multi-Layer Perceptron attain the highest accuracy.
Notes: Vanhoenshoven, F (reprint author), Univ Hasselt, Campus Dicpenbeek Agoralaan Gebouw D, BE-3590 Diepenbeek, Belgium. frank.vanhoenshoven@uhasselt.be; gonzalo.napoles@uhasselt.be; rfalcon@uottawa.ca; koen.vanhoof@uhasselt.be; mkoeppen@ieee.org
URI: http://hdl.handle.net/1942/23181
DOI: 10.1109/SSCI.2016.7850079
ISI #: 000400488301120
ISBN: 9781509042401
Category: C1
Type: Proceedings Paper
Validation: ecoom, 2018
Appears in Collections: Research publications

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