Sybil-proof your token airdrop with ManyMe
Leverage our custom sybil attacker analysis to protect your token distribution from airdrop farmers.
Our methodology
Most tokenless protocols and platforms are heavily farmed by a handful of users expecting to be rewarded with a token airdrop. These users usually create clusters of addresses they use to interact with the protocol and maximize their token allocation.
Although these incentive systems are good to bolster the use of the protocol, these malicious users usually claim their tokens, dump them and move on to the next protocol. Disproportionately rewarding this mercenary behavior can contribute to bad price action and community upheaval.
We have developped an automated address tracking system leveraging statistical analysis and AI to detect sybil clusters using transaction data from EVM chains. Forget about hiring a data scientist or indexing the blockchain and get the data you need now.
In a few minutes, you can identify the clusters of addresses likely to be owned by a single entity, making it easy to eliminate them and to get a fairer airdrop for your community.
Customization features
Custom confidence levels
Tune the confidence of the detection algorithm to adapt your identification process. Higher confidence will lead to stricter criterion for identification: thus lower the chances of falsely flagging a sybil cluster at the expense of flagging less potential sybils.
Multi-chain identification
Choose the chains on which the analysis runs to track addresses across different EVM networks. Enabling more chains may lead to uncovering more clusters.
Pricing
Standard
FAQ
How are airdrop savings calculated?
We adopt a one-address-one-user standard wherein we retain the address with the top airdrop allocation per sybil cluster identified. The estimated savings realized are the amount of tokens to be distributed to the addresses removed.
For example, if 2 clusters are detected:
- Cluster #1: 3 addresses with allocations of 100, 200, and 50 tokens we retain the address with 200 tokens, cluster savings are 100+50 = 150 tokens
- Cluster #2: 2 addresses with allocations of 1,000 and 2,000 tokens we retain the address with 2,000 tokens, cluster savings are 1,000 tokens
In case the allocation per address is unknown, we take the average tokens distributed per address as a reference.
How are sybil clusters identified?
Each user's entire on-chain footprint is thoroughly analyzed leveraging statistical and Machine Learning techniques to identidy Sybil clusters.