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MUD - Malicious URL Detector
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  • Introduction:
    Instant website evaluation for identifying harmful URLs.
  • Category:
    AI Detector
  • Added on:
    Jun 04 2024
  • Monthly Visitors:
    0.0
  • Social & Email:
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MUD - Malicious URL Detector: An Overview

MUD, or Malicious URL Detector, is an advanced Chrome extension that analyses web URLs in real-time to identify potential threats. By employing sophisticated Machine Learning algorithms and evaluating 24 distinct factors, MUD effectively warns users about phishing attempts and other online dangers, thereby enhancing their security while browsing the internet.

MUD - Malicious URL Detector: Main Features

  1. Real-time URL scanning
  2. Advanced threat detection algorithms
  3. Comprehensive analysis based on 24 factors
  4. User-friendly interface for quick evaluations

MUD - Malicious URL Detector: Use Cases

  1. Checking suspicious links in emails
  2. Verifying unknown websites before visiting
  3. Protecting sensitive information during online transactions
  4. Enhancing overall internet safety for everyday users

MUD - Malicious URL Detector: User Guide

  1. Install the MUD Chrome extension from the Chrome Web Store.
  2. Open the extension by clicking on the MUD icon in your browser's toolbar.
  3. Input the URL you wish to analyze into the provided text box.
  4. Click on the "Analyze" button to initiate the scanning process.
  5. Review the security evaluation and follow any recommended actions.

MUD - Malicious URL Detector: User Reviews

  • "MUD has saved me from clicking on several phishing links. It's a must-have for anyone who browses the web frequently!" - Jane D.
  • "The real-time scanning feature is fantastic. I feel much more secure while browsing." - Tom K.
  • "Easy to use and highly effective. I recommend it to all my friends." - Sarah L.

FAQ from MUD - Malicious URL Detector

What criteria does MUD use to evaluate the risk of a URL?
MUD evaluates URL safety by analyzing a variety of parameters, incorporating elements such as behavioral patterns, prior incident records, and sophisticated machine learning techniques.
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