Abstract
In this era of technology, phishing emails remain a critical cybersecurity threat, exploiting human vulnerabilities to compromise sensitive data and systems. Traditional detection methods, such as blacklists and static heuristics, often fail to keep pace with the evolving sophistication of phishing tactics. We propose RealPhish, a real-time phishing detection algorithm combining machine learning, Natural Language Processing (NLP), and rule-based heuristics to identify malicious emails with high precision. RealPhish analyzes both static email features and simulated user interaction data to enhance detection accuracy. Using a publicly available phishing email dataset and synthetically generated behavioral data, the algorithm achieves a detection accuracy of 95%, with a precision of 96% and recall of 89%, outperforming baseline models. The system also includes a rule-based override layer for known threats and provides interpretable outputs for transparency.
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@inproceedings{chapagain2025realphish,
title = {RealPhish: An Algorithm for Real-Time Email Phishing Detection},
author = {Chapagain, Devendra and Bhusal, Bishnu and Kshetri, Naresh and Subedi, Pradip},
booktitle = {Proceedings of the 26th Annual Conference on Information Technology Education},
year = {2025},
url = {https://doi.org/10.20944/preprints202511.0218.v1}
}