Article
Version 1
Preserved in Portico This version is not peer-reviewed
Phishing Emails Detection in Cyber Security
Version 1
: Received: 24 April 2024 / Approved: 25 April 2024 / Online: 28 April 2024 (04:54:11 CEST)
How to cite: Sharma, S.; Sharma, R.; Sharma, M. Phishing Emails Detection in Cyber Security. Preprints 2024, 2024041655. https://doi.org/10.20944/preprints202404.1655.v1 Sharma, S.; Sharma, R.; Sharma, M. Phishing Emails Detection in Cyber Security. Preprints 2024, 2024041655. https://doi.org/10.20944/preprints202404.1655.v1
Abstract
As digital communication becomes increasingly integral to personal and corporate activities, phishing attacks have emerged as a prevalent threat, ingeniously mimicking legitimate sources to illicitly acquire sensitive information. This research paper details the development of a sophisticated phishing detection application utilizing the DistilBERT-based model, finetuned on a diverse array of email datasets. The application significantly enhances the precision of phishing detection mechanisms, adeptly reducing the incidence of successful phishing attacks. Initial tests have demonstrated a precision rate of over 95% in detecting phishing emails, outperforming traditional rule-based filters substantially. The application exhibits robust defences against zero-day phishing attacks through its advanced machine learning framework, which dynamically adapts to emerging phishing strategies. This paper explores the methodology of developing the DistilBERT model, evaluates its efficacy against existing solutions, and discusses its implications for future cybersecurity practices. The study’s findings underscore the potential of AI-driven tools in transforming cybersecurity measures, offering a proactive approach to thwarting phishing attempts and safeguarding sensitive data.
Keywords
Phishing Detection; DistilBERT Model; Cybersecurity; Artificial Intelligence; Machine Learning; Email Security; Zero-Day Attacks; Natural Language Processing (NLP); Deep Learning; Cyber Threats
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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