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Enhancing Phishing Email Detection with Sentiment Analysis: A Hybrid Approach

Salian, SaiPrasad Sadashiv (2024) Enhancing Phishing Email Detection with Sentiment Analysis: A Hybrid Approach. Masters thesis, Dublin, National College of Ireland.

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Abstract

In this digital era, people are surrounded by technology and the internet usage has skyrocketed. This means that there’s more data available on the internet about a particular person than they can dream of. Cybercriminals use these data to launch attacks via emails and try to steal their sensitive information. Phishing is a very common type of attack used by these criminals and they try to attack large organizations in order to obtain ransom from them or to deal with their information. These attacks happening inside an organization through an organizational email has the potential to lead losses in billions. To detect such malicious attempt, this paper proposes a phishing email detection system that can analyse the sentiment behind the email and improve the level of accuracy. We use the DistilBERT model for sentiment analysis and then feed the sentiment aware embeddings to a SVM model for further classification. This proposed study provides steps on how to create a system that can not only check the regual heuristic features and key filters but also the sentiment behind the email which will help improve the overall security.

Item Type: Thesis (Masters)
Supervisors:
Name
Email
Prior, Michael
UNSPECIFIED
Uncontrolled Keywords: Phishing emails; DistilBERT; SVM; Sentiment Analysis; Phishing Detection
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > Electronic Mail
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > Electronic Mail
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Ciara O'Brien
Date Deposited: 31 Jul 2025 08:28
Last Modified: 31 Jul 2025 08:28
URI: https://norma.ncirl.ie/id/eprint/8363

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