IDEA: Enhancing Phishing Detection with Automated Twitter Bot Integration

[xdevman]

Problem Statement

Phishing attacks are a significant threat to Web3 users. These attacks often involve fraudulent websites that impersonate well-known platforms, aiming to steal sensitive information such as cryptocurrency keys. MetaMask eth-phishing-detect project plays a crucial role in identifying and blocking these malicious domains. However, the current method for reporting phishing URLs to this project requires users to manually create issues on the GitHub repository. This process can be cumbersome and may result in under-reporting of phishing attempts.

Proposed Solution

I have developed a Twitter bot designed to streamline the reporting of phishing attempts. Our solution leverages the widespread use of Twitter to quickly identify and report phishing URLs.

Key Features of the Twitter Bot

1. User-Friendly Reporting: Users can easily report phishing tweets by tagging the bot. This simplifies the reporting process, encouraging more users to contribute to the detection of phishing attempts.
2. Automated Data Collection: Once tagged, the bot collects the tweet's text, phishing link, username, tweet link, timestamp, and a screenshot. This data is stored in a MongoDB database.
3. Duplicate Detection: The bot checks for duplicate reports, ensuring that each phishing URL is reported only once, which helps in maintaining data integrity and reducing spam.
4. Validation and Reporting: Validated phishing URLs are automatically reported to the eth-phishing-detect GitHub repository by creating issues, thus integrating seamlessly with MetaMask existing workflow.

Benefits to MetaMask

• Enhanced Reporting Efficiency: Automating the reporting process leads to quicker detection and blocking of phishing domains.
• Increased User Participation: Simplifying the reporting process encourages more users to participate, resulting in a larger dataset of phishing attempts.
• Improved Data Quality: Automated duplicate detection and validation ensure high-quality, reliable data.

Implementation Plan

I have built a minimum viable product (MVP) model for saving the data and taking screenshots. However, due to limited access to API v2, I am unable to complete the full implementation at this time. I'm actively working to obtain access to API v2 and will update the system accordingly. Interested parties can monitor the progress of the project by visiting the GitHub repository at github.com/xdevman/phishguard.

I look forward to the opportunity to collaborate with the MetaMask team and contribute to this important initiative.