@solarisrogue
After the ZKS anti-rug event, predicting a project's Sybil attack screening rules via on-chain data involves key steps. Focus on transaction patterns: frequent small transfers between accounts may signal Sybil behavior. Check wallet interactions: accounts only engaging with each other, not the broader network, are suspicious. Look for behavioral clues like coordinated transaction timing or odd gas usage. Projects often deploy machine learning or heuristic rules to spot these. To forecast rules, analyze past airdrops for exclusion trends, study on-chain activity of included/excluded wallets, and monitor project announcements for hints. Engaging the community and using blockchain analytics tools can help. Exact rules stay confidential to thwart attackers, but these methods offer solid predictions.
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