Using On-chain Data Modeling to Identify Sybil Attacks: Trusta Case Study

As the threat of Sybil attacks looms over distributed storage systems and sensor networks, Trusta has found a solution with the help of AI and Footprint.

Sybil attacks are a common strategy employed by network attackers to gain network access by creating false identities. These attacks can disrupt network activity and, if enough false identities are created to win votes, even control the entire network. Sybil attacks are also widely prevalent in on-chain marketing activities, such as creating multiple wallets to exploit airdrop activities for NFTs or tokens and causing losses to project teams and communities.

Due to the anonymity and distributed nature of blockchain networks, developers typically need to employ multiple technical methods and conduct extensive data calculations and analysis to identify and prevent Sybil attacks.

Case Study: Trusta — AI-powered Sybil Prevention

Trusta has proposed an innovative solution using machine learning algorithms, neural network graphs, and zero-knowledge proofs to analyze user behavior patterns and identify which behaviors may be Sybil attacks.

However, the process of implementing this solution brings some challenges. The raw data on-chain is very complex and requires a lot of resources for data parsing. In addition, synchronizing data takes a long time and can be costly.

To help clients overcome these challenges, Footprint provides a solution for batch downloading on-chain data, simplifying the data collection process. It also supports access to all levels of data, including raw and structured data, making it easy to process data for accurate detection of Sybil attacks, which is crucial.

Benefits of Footprint’s Batch Download

If historical data is obtained directly through RPC and other node providers, it often requires tens of thousands of dollars and takes several weeks to download.

Footprint’s batch download solution can 10x download speeds while costing only 20% of traditional nodes.

Through Footprint’s solution, Trusta Lab can reduce the resources and funding invested in data acquisition and processing, allowing them to focus on building AI models to solve user problems and provide powerful risk prevention solutions for on-chain project teams and users.

Contact Footprint at [email protected]