Sybil Resistance
In web3, incentive programs are crucial for growing your community and rewarding users. However, they can be targets for Sybil attacks, where malicious individuals create multiple identities or use bots to steal rewards. With Fuul's Sybil Resistance tools, you can allow only legitimate users to receive rewards, protecting your budget and maintaining the integrity of your project.
How does Fuul prevent Sybil Attacks?
Fuul uses proprietary software to defend your incentive program against Sybil attacks at both preventive and proactive levels.
Preventive: Implement payout caps to limit excessive rewards and protect your budget.
Proactive: Detect self-referrals and wallet clusters by analyzing both frontend and onchain data.
Behavioral Cluster Detection
Fuul leverages a proprietary ML model that analyzes 30+ onchain behavior signals to detect clusters of wallets likely controlled by the same user. Key indicators include shared funding sources, similar transactions, and overlapping wallet activity.
Self Referral Detector
Fuul uses internal systems to detect when users are self-referring through multiple wallet addresses, ensuring that referrals remain fair and authentic across your program.
Bot Activity Detector
Fuul detects when automated tools interact with your program for unfair gains. The system identifies bot-driven actions and helps block them to maintain genuine engagement.
Payout Caps
You can limit the maximum amount of rewards to be collected by users. These payout caps can be applied to discretionary time windows (for example, a 100 dollar monthly cap).
Cap per end user
Define the maximum amount a single end user can earn through your incentive program, ensuring fair rewards and preventing abuse.
Cap per referral
Set the maximum total a referrer can earn from all referred users combined, maintaining balanced rewards across your referral network.
Cap per wallet
Limit the total earnings a wallet can accumulate, whether from referrals or as an end user, to avoid overlapping or duplicated payouts.
Why It’s Important to Protect Your Incentive Program
A good incentive program helps attract users and grow your community. But without proper protection, it can be abused, causing financial loss and damaging trust.If projects don’t take Sybil detection seriously, it’s the end users who end up paying the price, as their rewards and experience are affected.
Results & Reporting
Fuul's Sybil resistance system undergoes a thorough validation process. When accounts are flagged as potentially fraudulent, they are subjected to a detailed review to identify any signs of abuse.
Our reports clearly outline the Sybil accounts detected by our tools, giving you confidence in identifying the genuine users who are truly interested and active in your program. Any suspicious activity detected is excluded from the final reward calculations.
Continuous Monitoring
Reward distribution is not a one-time event; it requires continuous monitoring based on real-time metrics. As our models gather more data and adjust, we anticipate improvements in accuracy.