PhishGuard: Machine Learning Based Web Phishing Detection

HARISH M; KARTHIK K R; NITHIN A B; SUSHMA K V; VARSHA S

doi:10.36948/ijfmr.2025.v07i06.62317

PhishGuard: Machine Learning Based Web Phishing Detection

Author(s)	Mr. HARISH M, Mr. KARTHIK K R, Mr. NITHIN A B, Ms. SUSHMA K V, Ms. VARSHA S
Country	India
Abstract	Phishing attacks continue to be one of the most prevalent and dangerous cybersecurity threats, where malicious actors disguise fraudulent websites as legitimate ones to deceive users and steal sensitive information such as login credentials, financial details, and personal data. To address this growing challenge, this paper introduces PhishGuard, an intelligent phishing detection system built using machine learning techniques—specifically, the Random Forest and XGBoost (Extreme Gradient Boosting) algorithms—to accurately classify and detect phishing URLs. PhishGuard’s development process begins with data collection from reliable open-source repositories such as PhishTank and OpenPhish, ensuring a diverse and up-to-date dataset of both phishing and legitimate URLs. A comprehensive feature extraction phase then identifies critical characteristics that distinguish malicious sites from genuine ones, including domain-based, URL-based, and content-based attributes. Both Random Forest and XGBoost models were trained and evaluated using standard performance metrics. The results demonstrated that the combined approach achieves high accuracy, precision, and recall, effectively identifying phishing attempts even in real-world scenarios where attackers evolve rapidly. By automating the detection process and reducing human error, PhishGuard offers a practical and scalable solution to safeguard users from online fraud, emphasizing how ensemble learning techniques can strengthen cybersecurity defenses and ensure safer online interactions. Additionally, PhishGuard is implemented as a browser extension, enabling real-time phishing URL detection directly within users’ browsers.
Keywords	Phishing detection, Machine learning, Random Forest, XGBoost, Ensemble learning, URL classification, Feature extraction, Cybersecurity, Real-time detection.
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-12-05
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.62317

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PhishGuard: Machine Learning Based Web Phishing Detection

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