Phishing Detection System Using Machine Learning

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Author(s): Aparna Datta (Meghnad Saha Institute of Technology, India)and Michael Gomes (Meghnad Saha Institute of Technology, India)
Copyright: 2025
Pages: 24
Source title: Effective Strategies for Combatting Social Engineering in Cybersecurity
Source Author(s)/Editor(s): Rajeev Kumar (Moradabad Institute of Technology, India), Saurabh Srivastava (Moradabad Institute of Technology, India)and Ahmed A. Elngar (Beni-Suef University, Egypt)
DOI: 10.4018/979-8-3693-6665-3.ch015

Keywords: Cyber and Network Security / Cyber Behavior / Information Science Reference / Social Sciences & Humanities

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Abstract

Phishing attacks pose significant threats by tricking individuals and organizations into divulging sensitive information through deceptive websites and emails. This research paper presents a Phishing Detection System (PDS) designed to mitigate such threats using advanced machine learning techniques. The system's architecture includes data collection, feature extraction, a detection engine, and a response module. It employs machine learning algorithms such as Naive Bayes, K-Nearest Neighbors, Decision Tree, and Random Forest to optimize detection accuracy. The PDS provides real-time alerts, blocks malicious sites, and quarantines suspicious emails, achieving high detection accuracy with minimal false positives and negatives. Future enhancements focus on scalability, advanced machine learning integration, and user education. This system represents a significant advancement in cybersecurity by offering robust protection and actionable insights against phishing threats.

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Phishing Detection System Using Machine Learning

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