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Using Machine Learning to Detect Malicious URLs

There is a need for better predictive model that reduces the number of malicious URLs being sent through emails. This system should learn from existing metadata about URLs. The ideal solution for this problem would be able to learn from its predictions. For example, if it predicts a URL to be malicious, and that URL is deemed safe by the sandboxing environment, the predictor should refine its model to account for this data. The problem, then, is to construct a model with these characteristics that can make these predictions for the vast number of URLs being processed. Given that the current system does not employ machine learning methods, we intend to investigate multiple such models and summarize which of those might be worth pursuing on a large scale.

Identiferoai:union.ndltd.org:CLAREMONT/oai:scholarship.claremont.edu:cmc_theses-2755
Date01 January 2017
CreatorsCheng, Aidan
PublisherScholarship @ Claremont
Source SetsClaremont Colleges
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceCMC Senior Theses
Rights© 2017 Aidan J Cheng, default

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