Understanding Sybil Detection in Fuul

Sybil attacks can pose a serious threat to Web3 projects by allowing bad actors to exploit incentives programs with multiple fake identities. This guide will walk you through how Fuul's sybil detection tools help protect your incentive programs and ensure fair rewards distribution.

Understanding Sybil Attacks

A sybil attack occurs when someone creates multiple fake identities to exploit your rewards system. In Web3, this could involve creating multiple wallet addresses to claim tokens from an airdrop or manipulating staking rewards by splitting funds across different identities. This undermines the integrity of your program, making it critical to implement strong sybil resistance measures.

How Fuul Helps Protect Against Sybil Attacks

• Behavioral Cluster Detection: Fuul uses machine learning to monitor over 30 on-chain behavior signals, such as funding sources and transaction patterns, to identify clusters of wallets that likely belong to the same user. This helps automatically disqualify these accounts from receiving rewards.
• Self-Referral and Bot Activity Detection: Fuul's tools detect self-referrals and automated software activity by analyzing both front-end and on-chain data, ensuring rewards are given only to real, active users.
• Payout Caps: You can set custom payout caps to limit the rewards a single account can receive, applying these caps to specific time windows (e.g., monthly) to prevent any one account from draining the rewards pool.
• Continuous Monitoring and Reporting: Fuul’s system continuously adapts based on real-time metrics, providing detailed reports on flagged accounts. This gives you full visibility and control over who receives rewards.

How can you see everything that Fuul detects

In the Fraud Detection tab you will be able to see everything that Fuul detects and blocks.

You will be able to view transactions marked as pending, and decide whether to approve or reject them. You will also see transactions that have been automatically declined.

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