AI-Powered Phishing Attack Real Time For User Proctection

This research paper presents PhishShield AI, a hybrid and explainable phishing detection system designed to address the growing challenges of modern cyber threats. The system combines rule-based heuristic analysis with machine learning and natural language processing techniques to accurately detect phishing attacks in real time. Unlike traditional approaches that provide only binary results, the proposed system focuses on Explainable Artificial Intelligence (XAI), offering clear and human-readable explanations that help users understand the nature of detected threats.The system analyzes URLs, email content, and web pages by examining structural patterns, textual features, and hidden elements such as malicious forms. It integrates multiple detection layers to improve accuracy and ensure reliable performance, even against zero-day phishing attacks. Additionally, the inclusion of a user-friendly interface and a gamification component enhances user engagement and promotes cybersecurity awareness.Overall, this work aims to provide an effective, accessible, and intelligent solution that not only improves phishing detection but also educates users, enabling them to make safer decisions in the digital environment

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-28
DOI
https://doi.org/10.5281/zenodo.19862959
Primary Topic
Spam and Phishing Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Powered Phishing Attack Real Time For User Proctection

Hardik Chavan
Zenodo (CERN European Organization for Nuclear Research)
Spam and Phishing Detection
article

AI-Powered Phishing Attack Real Time For User Proctection

Hardik Chavan
article en

Abstract

This research paper presents PhishShield AI, a hybrid and explainable phishing detection system designed to address the growing challenges of modern cyber threats. The system combines rule-based heuristic analysis with machine learning and natural language processing techniques to accurately detect phishing attacks in real time. Unlike traditional approaches that provide only binary results, the proposed system focuses on Explainable Artificial Intelligence (XAI), offering clear and human-readable explanations that help users understand the nature of detected threats.The system analyzes URLs, email content, and web pages by examining structural patterns, textual features, and hidden elements such as malicious forms. It integrates multiple detection layers to improve accuracy and ensure reliable performance, even against zero-day phishing attacks. Additionally, the inclusion of a user-friendly interface and a gamification component enhances user engagement and promotes cybersecurity awareness.Overall, this work aims to provide an effective, accessible, and intelligent solution that not only improves phishing detection but also educates users, enabling them to make safer decisions in the digital environment

Zenodo (CERN European Organization for Nuclear Research)
MIT Art, Design and Technology University (IN)
Openalex Percentile: Top 2%
Spam and Phishing Detection
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