Project Name: Automatic TRON Address Clustering Project Track: Web3 Team Name: MistTrack Team Team Member(s): 6 DoraHacks Project Link: TRON Address Clustering | Buidls | DoraHacks
Project Goal: Our goal this time is to present an advanced system utilizing machine learning and graph computing to analyze attributes of TRON addresses to identify common user addresses, hot wallet addresses, and cold wallet addresses in the TRON ecosystem. At the same time, our analysis will examine each address’s characteristics and relationships to aid in the tracking of hacker addresses and activity, allowing relevant personnel to access additional insight into a wallet. Project Info:
In this age of information boom, the SlowMist security team discovered that malicious actors frequently utilize social media, phishing websites, and other methods to steal users’ digital assets. Our address identification mechanism primarily uses graph and machine learning algorithms to evaluate the public transaction history of TRON’s users and label their addresses into 3 separate categories: Hot wallet, Cold wallet, and Common users.
This is accomplished by combining the graph algorithm with node mining techniques to execute large-scale mining of transaction attributes of user address transactions. This also has the ability to perform influence analysis, community discovery based on tag propagation algorithm, and transaction path discovery based on graph traversal algorithm.
Welcome to HackaTRON S4.
Please so what wallets are going to fall under common and where will hackers’ wallets fall, will there be any tag to identify their addresses.
Security is an important aspect in web3 that needs a lot of improvement. Happy to see that your project works towards that direction.
I wanted to know what aspects of an address are tracked and based on which factors are the categories decided. And before that, yes, what is the meaning of each category?
It’s good to see that the project is focused on developing a mechanism that can be used to protect users’ digital assets and prevent malicious actors from stealing them.
I would like to know that what steps are being taken to ensure the privacy and security of user data while mining transaction attributes of user address transactions? Are there any potential risks or vulnerabilities that need to be addressed?
Hello, you have participated with the same project during season 2 and 3. Participants who have already presented a same project in the previous seasons have to go to the builder track and explain what are the improvements.
