Date of Award
8-2016
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Interdisciplinary Studies
First Advisor
Victor Raskin
Second Advisor
Julia M. Taylor
Committee Chair
Victor Raskin
Committee Co-Chair
Julia M. Taylor
Committee Member 1
James E. Dietz
Committee Member 2
John A. Springer
Abstract
This dissertation investigates whether or not malicious phishing emails are detected better when a meaningful representation of the email bodies is available. The natural language processing theory of Ontological Semantics Technology is used for its ability to model the knowledge representation present in the email messages. Known good and phishing emails were analyzed and their meaning representations fed into machine learning binary classifiers. Unigram language models of the same emails were used as a baseline for comparing the performance of the meaningful data. The end results show how a binary classifier trained on meaningful data is better at detecting phishing emails than a unigram language model binary classifier at least using some of the selected machine learning algorithms.
Recommended Citation
Falk, Courtney, "Knowledge modeling of phishing emails" (2016). Open Access Dissertations. 754.
https://docs.lib.purdue.edu/open_access_dissertations/754