Alabi, Oluwafunsho John (2024) A Resilient NLP-Based Detection System of Phishing Emails Leveraging Deep learning Technique. Masters thesis, Dublin, National College of Ireland.
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Abstract
Phishing attacks have become more sophisticated over time, causing a significant threat to both individuals and organizations globally. As attackers create new techniques, it's essential to stay ahead of the trend and invest in robust cybersecurity measures.
A promising approach is to leverage natural language processing (NLP) and deep learning techniques to create a cutting-edge detection system. This research aimed to do just that by analyzing current methods, identifying gaps, and introducing innovative solutions to improve phishing email detection accuracy. The NLP-based system has enormous potential in detecting deceiving content and neutralizing novel threats. To ensure its efficacy, it was subjected to rigorous testing and analysis, simulating real-world scenarios. This significantly contributed to the field of cybersecurity by strengthening defenses against phishing attacks and fostering a safer online environment for everyone. This is better than previous detection systems as it is more accurate, achieving an 84%accuracy.
Item Type: | Thesis (Masters) |
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Supervisors: | Name Email Sahni, Vikas UNSPECIFIED |
Uncontrolled Keywords: | NLP Detection System; Deep learning technique; Resilient; Cybersecurity; Ethical considerations |
Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security 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: | 03 Jun 2025 15:00 |
Last Modified: | 03 Jun 2025 15:00 |
URI: | https://norma.ncirl.ie/id/eprint/7733 |
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