Computer Science > Cryptography and Security
[Submitted on 1 Nov 2024
]
Title: A Machine Learning Driven Website Platform and Browser Extension for Real-time Scoring and Fraud Detection for Website Legitimacy Verification and Consumer Protection
Title: 基于机器学习的网站平台和浏览器扩展程序，用于实时评分和网站合法性验证及消费者保护的欺诈检测
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.
Submission history
From: Md Kamrul Hasan Chy [view email][v1] Fri, 1 Nov 2024 05:13:18 UTC (517 KB)
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