Knowledge modeling of phishing emails

<p>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.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Interdisciplinary Studies

Year

2016

Author dc:creator

• Falk, Courtney

Contributors dc:contributor

• Victor Raskin
• Julia M. Taylor
• James E. Dietz
• John A. Springer

• Information Technology
• Communication and the arts
• Applied sciences
• Knowledge representation
• Natural language processing
• Ontology
• Phishing
• Security
• Semantics
• Computer Sciences
• Information Security

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/754

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-1946