Fraud URL Detection

Abstract

Fraud detection in digital transactions is a critical concern for financial institutions, e-commerce platforms, and other online service providers. With the increasing sophistication of cybercrimes, traditional fraud detection methods are becoming less effective. This study presents a novel approach to fraud detection using URL link analysis. The proposed system analyzes URLs associated with transaction requests, extracting features such as domain reputation, link structure, and associated metadata to identify potential fraudulent activities. By leveraging machine learning techniques such as decision trees, random forests, and neural networks, the system is able to classify URLs as legitimate or fraudulent with a high degree of accuracy. The proposed method provides an additional layer of security for online transactions, reducing the risk of fraud and enhancing user trust in digital platforms.

Introduction

Summary:
As e-commerce rapidly grows, online fraud—including phishing, deceptive ads, and identity theft—has become a serious threat causing financial losses and eroding consumer trust. Fraudsters exploit various platforms such as classified ads, e-commerce sites, and dating apps with increasingly sophisticated tactics.

Literature Review & Key Findings:

• Phishing detection methods have evolved from simple URL analysis to advanced machine learning and deep learning models that analyze lexical, host-based, behavioral, and contextual features.

• Hybrid models combining multiple detection techniques perform better than single approaches.

• Emerging technologies like blockchain offer promising ways to improve URL trust verification.

• Challenges remain due to evolving phishing techniques, URL obfuscation, and lack of real-time labeled data.

Methodology for Phishing Detection:

1. Data Collection: Gather large datasets of URLs labeled as safe or phishing from sources like PhishTank and VirusTotal.

2. Data Cleaning: Remove duplicates, fix broken links, and expand shortened URLs.

3. Feature Extraction: Analyze URL characteristics such as length, use of HTTPS, presence of suspicious symbols, subdomains, and IP addresses instead of domain names. Features may be engineered for better detection (e.g., counting suspicious keywords or measuring entropy).

4. Model Selection & Training: Use machine learning algorithms like decision trees, random forests, SVM, neural networks, or XGBoost to train on extracted features.

5. Evaluation: Measure model performance with accuracy, precision, recall, and F1 scores.

6. Real-Time Testing & Blacklists: Optionally deploy models in live environments, cross-check predictions with blacklists like Google Safe Browsing.

7. Continuous Learning: Update models over time based on new data and mistakes to improve detection.

Conclusion

Fraud detection through URL analysis is a crucial area in cybersecurity, especially with the rise of phishing attacks, malware distribution, and online scams. From the reviewed literature, it’s clear that using URL-based features—such as link structure, domain characteristics, and lexical patterns—can provide powerful indicators of whether a link is safe or malicious. In the future, integrating behavioral analysis, real-time detection systems, and decentralized trust models (like blockchain) may offer even stronger protection. Still, maintaining adaptability and minimizing false positives remain key challenges. Overall, URL-based fraud detection continues to evolve and remains a vital part of securing the digital world.

References

[1] Alqahtani, F., &Alghamdi, A. (2021). A survey on phishing detection methods. International Journal of Computer Applications, 175(8), 1-6. https://doi.org/10.5120/ijca2021920395 [2] Bhattacharya, A., &Kundu, S. (2021). Blockchain technology for phishing detection. Computers, Materials & Continua, 68(2), 1677-1689. https://doi.org/10.32604/cmc.2021.015292 [3] Bhandari, D., &Pandya, M. (2020). Blockchain-based solutions for phishing detection. International Journal of Computer Science and Applications, 17(3), 20-31. https://doi.org/10.12792/ijcsa.17.3.20 [4] Chen, Y., &Xu, Z. (2019). A comprehensive review of URL classification for phishing detection. International Journal of Cyber-Security and Digital Forensics, 8(3), 214-229. https://doi.org/10.1504/IJCSDF.2019.099707

Copyright

Copyright © 2025 Mr. Siddhraj Nalawade, Mr. Saurabh Narayane, Mr. Abhijit Nawale, Mr. Amar Parbat, Prof. Afrin Shaikh. 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.