Decoding MetaHub's Anti-fraud Arsenal: Unlocking  the Power of BMAS

There can be no doubt that security, transparency, and trust are of the utmost importance in the still-evolving world of Web3 and decentralized communities. A fundamental element of these communities is BOT Detection, which is the process of distinguishing between human users and automated bots on websites, applications, or digital platforms. Using two layers of technology, MetaHub has developed a Bot Detection system that incorporates the Bot Mitigation and Anti-fraud System (BMAS). In this article, we will explore BMAS in more detail and see why it has become one of MetaHub’s primary Unique Selling Points (USPs).

What is BMAS and how does it work?

As blockchain technology develops, cyber threats and fraudulent activities become increasingly prevalent. A BMAS is a combination of Bot Mitigation and Anti-Fraud Systems (BMAS) that work together to protect Web3 platforms by reducing the risk of automated bot attacks and safeguarding websites, mobile apps, and visitors from abuse. BMAS system is used to not only stop malicious bots and prevent fraud threats before they impact our ecosystem but also detect fraudulent activity. In particular, to detect fraud, there are several techniques that we are implementing:

• Calculate statistical parameters
• Match data
• Perform Logistic Regression analysis
• Use probability models and distributions
• Mine data to classify, segment, and cluster data to find associations and rules signaling patterns of malicious activities
• Detect fraud with heuristic rules, machine learning, and thresholds

BMAS system will need data to get started. The more data we feed into the system to start with, the more accurate the results will be. Therefore, simulated fraud data is also fed into the system to ensure the volume, quality, and diversity in the analysis process.

The development potential of BMAS

Over the recent years, numerous big enterprises across various industries have opted to utilize BMAS to minimize the risks associated with bot and fraudulent activities. An example is Alibaba Cloud Anti-Bot Service, which offers comprehensive anti-bot protection for web applications, HTML5 websites, mobile apps, and APIs.  This solution has already been adopted by notable enterprises like Reddit, Patreon, and AngleList. The BMAS approach holds enormous potential for reshaping behavior management within Web3. BMAS proves its adaptability across various scenarios thanks to its robust machine learning capabilities and real-time monitoring, making it an invaluable asset for enterprises and projects:

• Intrusion and Fraud Prevention
• Enhance User Experience
• Customization and Flexibility
• Support Project Enhancement and Business Development

MetaHub’s BMAS and the reason why it stands out

MetaHub’s BMAS serves as the guardian of the MetaHub ecosystem. It’s a comprehensive system designed to detect and mitigate the activities of automated bots and prevent fraudulent actions within the Web3 platform and it works through five core components:

• User Interaction Monitoring: It monitors user interactions with a system, capturing crucial data like the SoulBound Token and Transaction Data for user authentication and behavior analysis.