KOKOSde

KOKOSde / onchain-sybil-detector

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Open-source behavioral Sybil attack detection for blockchain networks

18
1
100% credibility
Found Mar 28, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

An open-source tool that analyzes blockchain wallet transactions to detect and explain coordinated fake account groups abusing airdrops and incentives.

How It Works

1
📰 Discover the tool

You hear about a free open tool that spots groups of fake wallets cheating airdrops and rewards on blockchains.

2
💻 Set it up easily

Download the tool to your computer – it starts working instantly with built-in practice examples, no extras needed.

3
🧪 Create test examples

Make pretend wallet histories with sneaky fake groups mixed in to safely practice spotting them.

4
🔍 Check your wallet list

Feed in addresses from your blockchain rewards list, pick the network, and let it hunt for coordinated fakers.

5
📊 Review the findings

Open a simple report showing suspicious wallet clusters, their timelines, funding links, and matching behaviors.

✅ Secure your rewards

Remove the fake groups confidently, keeping your airdrop or rewards fair for real users.

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Star Growth

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AI-Generated Review

What is onchain-sybil-detector?

This open-source Python tool detects Sybil attacks in blockchain networks by clustering EVM wallets based on behavioral signals like timing, gas patterns, and funding flows. It scans address lists or transaction CSVs across chains like Ethereum, Base, BNB, Arbitrum, Optimism, and Polygon, outputting HTML reports with interactive graphs, JSON data, and explainable evidence. Users get CLI commands for quick scans, airdrop hunting, and offline synthetic data generation—no APIs needed to start.

Why is it gaining traction?

As a free open-source alternative to $100k/year enterprise Sybil detectors, it delivers reproducible benchmarks crushing naive baselines on adversarial setups up to level 8 (delayed coordination, chain hopping). Developers love the offline-first design, multi-chain support, and analyst-ready outputs like per-cluster timelines and funding graphs. It's battle-tested with 51 passing tests and MIT license for easy forking.

Who should use this?

DeFi protocol risk engineers auditing airdrops, exchange trust-and-safety teams hunting referral farmers, and DAO operators spotting governance abusers. Security researchers benchmarking behavioral detection will appreciate the synthetic generators and CLI for campaign scans.

Verdict

Grab it for prototyping Sybil defenses—docs are thorough, tests solid, but with 18 stars and 1.0% credibility score, treat as experimental. Pair with your own validation before production.

(187 words)

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