mlsanityData to 14 Oct 2026

A Machine Learning Driven Website Platform and Browser Extension for Real-time Scoring and Fraud Detection for Website Legitimacy Verification and Consumer Protection

arXiv 2411.00368 ↗First posted 1 Nov 2024cs.CR cs.LGcited by 0

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Abstract (arXiv)

This paper introduces a Machine Learning-Driven website Platform and Browser Extension designed to quickly enhance online security by providing real-time risk scoring and fraud detection for website legitimacy verification and consumer protection. The platform works seamlessly in the background to analyze website behavior, network traffic, and user interactions, offering immediate feedback and alerts when potential threats are detected. By integrating this system into a user-friendly browser extension, the platform empowers individuals to navigate the web safely, reducing the risk of engaging with fraudulent websites. Its real-time functionality is crucial in e-commerce and everyday browsing, where quick, actionable insights can prevent financial losses, identity theft, and exposure to malicious sites. This paper explores how this solution offers a practical, fast-acting tool for enhancing online consumer protection, underscoring its potential to play a critical role in safeguarding users and maintaining trust in digital transactions. The platform's focus on speed and efficiency makes it an essential asset for preventing fraud in today's increasingly digital world.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 45 references that are in the map. First posted on arXiv 1 Nov 2024.

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References · 2

  1. 2021Credit Card Fraud Detection using Machine Learning: A Studycited by 6
  2. 2024Proactive Fraud Defense: Machine Learning's Evolving Role in Protecting Against Online Fraudcited by 1

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