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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM53719
Volume:
9 |
11 |
Nov 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
In today’s fast-paced digital environment, phishing remains a persistent and damaging threat. Over 90% of successful cyberattacks originate from a “Human Error Gap,” such as clicking a deceptive link. To address this, PhishGuard AI has been developed—an AI-powered cybersecurity system designed to combat phishing through real-time detection and transparent user education. This intelligent system is capable of analyzing complex URL features to predict malicious intent with high accuracy (over 93%). Built using an optimized XGBoost Classifier for backend analysis and a React.js browser extension for an intuitive front-end interface, the system’s core innovation is its Explainable AI (XAI) module. This module translates complex predictive scores into clear, human-readable risk factors (e.g., “The URL contains the ‘@’ symbol”). This transparency serves as a real-time educational tool, empowering the user to understand threats and build digital resilience. This project explores the design, development, and deployment of this system, emphasizing the mitigation of human error by transforming security from a “black box” into a transparent educational partner.
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Publication Date
11/10/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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