Graduation Year

2017

Date of Submission

4-2017

Document Type

Campus Only Senior Thesis

Degree Name

Bachelor of Arts

Department

Computer Science

Reader 1

Mariam Salloum

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Terms of Use for work posted in Scholarship@Claremont.

Rights Information

© 2017 Aidan J Cheng

Abstract

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.

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