Study on Adversarial Robustness of Phishing Email Detection Models

Developing robust detection models against phishing emails has long been a main concerns of the cyber defense community. Currently public phishing/legitimate datasets are lack adversarial email examples which keeps the detection models vulnerable. To address this problem, we developed an augmented phishing/legitimate email dataset, utilizing different adversarial text attack techniques. In this work, the emails that can easily transform to adversarial examples and their unique characteristics have been detected and analyzed. Henceforth the models are retrained with adversarial dataset and the results show that ac- curacy from and F1 score of the models have been improved from five to forty percent under attack methods. In another experiment synthetic phishing emails are generated using a fine-tuned GPT-2 model. The detection model has retrained with newly formed dataset and we have observed the accuracy and robustness of the model has not improved under black box attack methods. In our last experiment we proposed a defensive technique to classify adversarial examples to their true labels using K-Nearest Neighbor with 94% accuracy in our prediction.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Houston

Year dc:date.issued

2022

Author dc:creator

• Mehdi Gholampour, Parisa

Advisor dc:contributor.advisor

• Verma, Rakesh M.

Committee members dc:contributor.committeemember

• Shi, Weidong
• Zhang, Yunpeng

• Adversarial attack
• Phishing detection

Statement dc:rights

• The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/10657/14462

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/14462