Airdrop farmers, you may want to know how Sybil detection works so you don’t get disqualified from the airdrop that you are investing your time and money into. Sybil detection checks for multiple wallets that were created by an individual for the purpose of earning more airdropped tokens.
Below are findings from Trusta, the company that Gitcoin used for their passport and that has a site to check wallet score (incl. Sybil detection) for zkSync, Linea, Starknet, Base, Scroll, and Manta:
The following is how Sybil detection works and how we can intelligently use this info.
Trusta’s AI-ML Sybil detection looks at:
I) Asset transfer relationships – token transfers between addresses and the first gas provision to an address.
II) Account behavior – interacting with the same contracts/methods with comparable timing and amounts. Variables considered include first and latest transaction dates, smart contracts interacted with, interaction amount, frequency, and volume.
Guidelines if you are farming
How Sybil Detection Works
Previous airdrops had
i) No Sybil resistance (eg. Uniswap, ENS)
ii) Community reporting (Safe, Optimism) or
iii) AI-ML algorithm (e.g. Arbitrum)
Trusta is advancing the AI-ML approach with a 2-phase framework to identify Sybil communities using clustering algorithms (see bold text for key factors):
Asset transfer graphs
Phase 1 analyzes asset transfer graphs (ATGs) with community detection algorithms like Louvain and K-Core to detect densely connected and suspicious Sybil groups.
Account behavior similarities
Phase 2 computes user profiles and activities for each address. K-means refines clusters by screening dissimilar addresses to reduce false positives from Phase 1.
StarLike Asset Transfer Graph:
ChainLike Asset Transfer Graph:
TreeLike Asset Transfer Graph: