PhishGuard: Phishing URL Detection Using Federated Learning and Transformer Networks

Abstract

Phishing attacks continue to be one of the major cybersecurity threats, where attackers use malicious URLs to deceive users and steal sensitive information. Most existing phishing detection systems rely on centralized data collection and traditional machine learning approaches, which raise serious privacy concerns and fail to effectively detect newly emerging or zero-day phishing URLs. This paper proposes a privacy-preserving phishing URL detection framework based on Federated Learning and Transformer models. The proposed approach enables collaborative training of detection models across multiple client devices without sharing raw user data. A Transformer-based deep learning model is used to automatically learn complex URL patterns and contextual dependencies. The system classifies URLs as legitimate, phishing, or potential zero-day phishing based on prediction confidence. A web-based interface is developed to provide real- time URL classification. The experimental design shows that combining federated learning with transformer models significantly improves detection performance while ensuring data privacy and scalability.

Introduction

Key features of PhishGuard:

• Transformer Encoder for Character-Level URL Classification: Treats each character in a URL as a token, capturing subtle manipulations like look-alike characters, hyphens, and misspellings. Multi-head self-attention layers extract long-range dependencies, producing rich contextual representations.
• Federated Learning (FedAvg): Multiple distributed clients train the global Transformer model collaboratively by sharing gradients instead of raw data, preserving privacy while leveraging diverse URL datasets.
• Three-Class Threat Taxonomy: URLs are classified as Safe, Phishing, or Zero-Day Alert, providing actionable insights and risk gradation rather than a simple binary decision.
• System Architecture: Modular design with a React.js frontend for URL submission and threat visualization, FastAPI backend for preprocessing and API management, a Transformer inference engine, and a Federated Learning coordination layer for collaborative training.
• Workflow: Character-level tokenization, Transformer inference with softmax probability outputs, heuristic augmentation for human-readable threat indicators, and color-coded verdict presentation.

Summary: PhishGuard effectively addresses the challenges of detecting evolving phishing URLs while maintaining privacy. It generalizes to novel threats, produces confidence-weighted three-class outputs, and integrates a scalable architecture combining state-of-the-art Transformer models with Federated Learning.

Conclusion

This paper presented PhishGuard, a phishing URL detection system that combines Transformer-based deep learning with Federated Learning to deliver privacy-preserving, high-accuracy, real-time threat classification. The system classifies URLs into three categories — Safe, Phishing, and Zero-Day Alert — providing actionable risk gradations that binary classifiers cannot offer. Experimental results demonstrate that PhishGuard achieves 97.6% accuracy on a three-class benchmark, outperforming centralized and classical baselines while preserving data privacy through the Federated Averaging protocol. The system is deployed as a full-stack web application with a FastAPI backend and a React.js frontend, enabling real-time URL analysis with confidence scores and human-readable threat indicators. Future work will explore several promising directions. First, differential privacy mechanisms such as Gaussian noise injection into gradient updates will be integrated to provide formal privacy guarantees for participating client nodes. Second, the system will be extended to support dynamic client participation, enabling asynchronous Federated Learning in which clients join and leave the training process without disrupting global model convergence. Third, cross-modal threat intelligence will be incorporated, combining URL-based signals with HTML content analysis and DNS record features to further improve zero-day detection. Fourth, adversarial robustness evaluations will be conducted to assess PhishGuard\'s resilience against URL obfuscation attacks specifically designed to evade Transformer-based detectors. Finally, the three-class taxonomy will be expanded to include additional threat categories such as malware distribution URLs and typosquatting domains.

References

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Copyright

Copyright © 2026 Ms. B. Santhoshi, A. Karthik, A. Yogyatha Sai Prasanna Lakshmi, Ch. Sri Devi, S. Sai Kumar. 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.