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

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          Abstract

          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.

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          Author and article information

          Journal
          01 November 2024
          Article
          2411.00368
          7c1ee0ae-01d4-4c8a-af3a-2b32a1de2341

          http://creativecommons.org/licenses/by/4.0/

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          Custom metadata
          Journal of Multidisciplinary Engineering Science and Technology (JMEST) 2024
          cs.CR cs.LG

          Security & Cryptology,Artificial intelligence
          Security & Cryptology, Artificial intelligence

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