Real-Time Sybil Detection On Base With GoldRush - (Part 2)
Sybil attacks are no longer just a minor nuisance in Web3 — they’re a systemic economic threat. Behind every fake wallet, there is a hidden cost: distorted metrics, misallocated incentives, compromised governance, and a false sense of adoption.
This piece takes you deeper into the mechanics of Sybil resistance: the economics, the forensics, the ML architectures, and finally a minimal yet powerful technical implementation you can use as a foundation for your own Sybil-detection pipeline.
Let’s begin where it matters most — the economics.
I. Measuring Sybil Defense: Why Metrics & Economics Matter More Than Accuracy
Sybil defense isn’t just a technical game — it’s an economic moat.
1.1 The Illusion of Adoption: Why Metrics Are Getting Destroyed
Sybil attackers create hundreds (or thousands) of wallets to farm incentives, artificially pumping your:
Daily active wallets
Transaction count
Retention curves
Liquidity provider numbers
A project might proudly report 100,000 users, when 70% are fake.
This distortion leads to:
Misleading investor decisions
Wasted incentive budgets
Wrong product decisions
A negative feedback loop attracting more Sybils
This is why Sybil resistance isn’t optional — it’s foundational economics.
1.2 Core Evaluation Metrics
Precision
Of all wallets you flagged, how many were truly Sybil?
Protects users, reputation, and trust.
Recall
Of all real Sybils, how many did you catch?
Prevents reward drain and treasury loss.
False Positive Rate (FPR)
How many genuine users you accidentally punish.
Too high → community outrage + reputational collapse.
Attacker Cost to Succeed (ACS)
How expensive the attack becomes.
The real secret sauce to kill Sybil farming.
ACS is the true north star.
If attacking becomes too expensive, attackers simply walk away.
II. On-Chain Forensics: Where Attackers Can't Hide
Modern Sybil detection has moved from “check wallet characteristics” to deep graph forensics.
2.1 Why Local Attributes Fail
Attackers now imitate real users:
Normal-looking transaction histories
Pattern-randomized timing
Diverse contract interactions
Local ML models (Bayesian classifiers, clustering, flag thresholds) break easily.
2.2 The Real Forensics: Behavior, Graphs & Synchronization Signals
Key Sybil Signals:
Synchronized behavior → identical transaction footprints
Fan-in / fan-out funding → one source funds many wallets
Wallet allocation similarity → identical token + balance structure
Activity simulation → fake transaction bursts to mimic DAUs
If you ever see:
250 wallets
All funded by the same address
Performing the exact same 5 transactions
Within the same 2–5 seconds
You are looking at a Sybil cluster.
III. Advanced Sybil Scoring Architecture (Human Words, Not Buzzwords)
Real attackers operate over weeks or months, not minutes.
So detection must understand:
Wallet history
Funding origin
Temporal patterns
Network relationships
Cross-chain movement
A modern scoring architecture looks like this:
CNN extracts spatial patterns from transaction matrices (“what happened and how often”).
BiLSTM learns temporal evolution (“when, before, after”).
Attention identifies suspicious time windows (“where the attack behavior peaks”).
K-Means cleans classification boundaries ("cluster Sybils tightly and isolate them").
At the end, you get a Sybil Risk Score (0–1) instead of a binary judgment.
IV. Technical Implementation
Below is your requested minimal implementation, showing how a real Sybil analytics pipeline can ingest raw on-chain data and compute behavioral signals.
This is intentionally lightweight but realistic.
Step 1: Environment Setup
Begin by installing Node.js (version 16+) and npm. Visit nodejs.org and download the LTS version. Verify installation:
node --version
npm --versionStep 2: Project Initialization
2.1) Create a new directory for your project:
mkdir sybil_attack
cd sybil_attackWhat this does: Creates a new folder for your project and enters it. All your files will be organized here.
2.2) Initialize npm and install dependencies:
npm init -y
npm install ws graphql-ws graphql axios ethers
npm install dotenvWhat this does:
npm init -y creates a package.json file (package configuration file) with default settings
npm install dotenv installs the dotenv package to safely load environment variables from .env file
2.3) Create a .env file in the root directory:
GOLDRUSH_KEY=your_api_key_here
BASE_RPC_URL=https://base-goerli.publicnode.com
ORACLE_PRIVATE_KEY=your_oracle_private_key What this does: Stores your Goldrush Streaming API key securely in a .env file. This file is ignored by Git (never committed) so your credentials stay private.
Obtain your Goldrush API key from goldrush.dev by registering as a developer. This key authenticates your requests to the Goldrush Streaming API.
Step 3: Project Structure
Organize your project with this directory structure:
sybil_attack/
├── .env
├── .gitignore
├── package.json
├── package-lock.json
├── stream.js
Step 4: Let’s deep dive into Coding
Create stream.js:
4.1 Pulling Wallet Activity Using Covalent Goldrush SDK : (Stream.js)
import 'dotenv/config';
import WebSocket from 'ws';
import { createClient } from 'graphql-ws';
import { GoldRushClient, StreamingChain } from '@covalenthq/client-sdk';
const API_KEY = process.env.GOLDRUSH_API_KEY;
// Use GoldRush SDK for streaming
const client = new GoldRushClient(API_KEY, {}, {
onConnecting: () => console.log("[GoldRush] Connecting..."),
onOpened: () => console.log("[GoldRush] Connected"),
onError: (e) => console.error("[GoldRush] Error", e),
onClosed: () => console.log("[GoldRush] Disconnected"),
});
// In-memory state for risk scoring
const walletState = new Map();
// Simple scoring: count txs in short window
function scoreWallet(addr) {
const state = walletState.get(addr);
if (!state) return 0.5;
const now = Date.now();
// count txs in last minute
const recent = state.lastTxs.filter(t => now - t < 60 * 1000).length;
// fewer txs → less risk in this simple model
const score = Math.max(0, 1 - recent / 10);
state.score = score;
return score;
}
// Handle each wallet activity event
function handleEvent(evt) {
const from = evt.from_address?.toLowerCase(),
to = evt.to_address?.toLowerCase();
for (const addr of [from, to].filter(Boolean)) {
if (!walletState.has(addr)) {
walletState.set(addr, { lastTxs: [], score: 0.5 });
}
const st = walletState.get(addr);
st.lastTxs.push(Date.now());
// keep only last ~5 min events
st.lastTxs = st.lastTxs.filter(t => Date.now() - t < 5 * 60 * 1000);
const newScore = scoreWallet(addr);
console.log(`Address ${addr} → new Sybil Risk Score: ${newScore.toFixed(2)}`);
// here you can call oracle, flag, etc.
}
}
This retrieves the entire transaction timeline of a wallet — essential for temporal Sybil analysis.
4.2 Subscribing to Wallet Activity with different Wallet Address
// Try ultra-active addresses on Base (token contracts with high volume)
const MONITORED_WALLETS = [
'0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913', // USDC on Base (extremely active)
'0x4200000000000000000000000000000000000006', // WETH on Base
'0x50c5725949A6F0c72E6C4a641F24049A917DB0Cb', // Aerodrome Router
'0xd9aAEc86B65D86f6A7B5B1b0c42FFA531710b6CA', // BaseName (.base domains)
];
console.log(`Monitoring ${MONITORED_WALLETS.length} high-activity addresses on Base...`);
console.log('Waiting for transactions...');
// Keep track of whether we receive any data
let receivedData = false;
// Subscribe to Wallet Activity stream
client.StreamingService.rawQuery(
`
subscription WalletTxs($chainName: ChainName!, $walletAddresses: [String!]!) {
walletTxs(chain_name: $chainName, wallet_addresses: $walletAddresses) {
tx_hash
from_address
to_address
value
block_signed_at
successful
}
}
`,
{ chainName: StreamingChain.BASE_MAINNET, walletAddresses: MONITORED_WALLETS },
{
next: (data) => {
receivedData = true;
console.log('Received data:', JSON.stringify(data, null, 2));
const ev = data.data?.walletTxs;
if (ev) {
console.log(`Processing ${ev.length} transaction(s)`);
for (const tx of ev) handleEvent(tx);
} else {
console.log('No walletTxs in data');
}
},
error: (err) => console.error("Stream error:", err),
complete: () => console.log("Stream completed"),
}
);
This finds clusters of wallets with identical first 5 transaction patterns — a major Sybil signal.
4.3 Adding a timeout check
// Add a timeout check
setTimeout(() => {
if (!receivedData) {
console.log('\n⚠️ No transactions received after 30 seconds.');
console.log('This might indicate:');
console.log(' 1. The streaming API requires specific subscription setup');
console.log(' 2. These addresses might not be configured for streaming');
console.log(' 3. There might be API limitations or delays');
}
}, 30000);Clean, modular, and extensible — exactly how real detection systems scale.
Now when we run : node stream.js , we get
V. Systemic Defenses:
Why Beating Sybil Attacks Requires More Than Just Machine Learning**
One of the biggest misconceptions in Web3 security is the belief that “a better model” will magically fix Sybil attacks.
It won’t.
Real Sybil resistance is not a model, it’s an ecosystem design problem.
Attackers don’t care about your ML accuracy — they care about whether they can profit.
That’s why modern Sybil defense is a full-stack system that blends economics, cryptography, and human judgment.
1. Token-Gating + Proof-of-Personhood (PoP): Raising Attacker Cost at the Source
Think of token-gating as the security guard at the door.
Proof-of-personhood is the moment the guard checks if you’re actually a real human.
Together, they raise the Attacker Cost to Succeed (ACS) like crazy.
A real user passes once and moves on.
A Sybil attacker needs to create hundreds of identities… and the cost multiplies.
This doesn’t need to be invasive or tied to your real identity — it can be a simple, optional, privacy-preserving check.
It doesn’t block people.
It just blocks cheap automation.
2. Progressive Incentives:
Reward the loyal, discourage the hit-and-run farmers**
If every new wallet gets equal rewards, who benefits most?
Sybils.
Every. Single. Time.
Progressive systems flip the economics:
Real users accumulate trust over time and unlock bigger rewards.
Fake users lose money trying to maintain hundreds of wallets for months.
This shrinks Sybil profitability without punishing newcomers.
It’s like loyalty points — not KYC.
Fair, simple, effective.
3. Human-in-the-Loop (HITL):
Because no model should have the power to punish a real user**
Even the best detection pipeline will occasionally make mistakes.
That’s just the nature of statistics.
But what matters is what happens after a user gets flagged.
A good system says:
“Hey, something looks unusual.
If this was you, here’s a quick way to confirm.”
A bad system says:
“Sorry, our model has decided you’re a bot. Goodbye.”
HITL makes the system humane.
It gives users an appeal path.
It keeps trust high.
And ironically, it makes attackers’ lives harder — because humans are really good at noticing “something feels off.”
Human judgment + ML → the best possible trust model.
VI. The Future of Sybil Resistance:
Where the Next Generation of Defense Will Come From**
The battlefield is changing.
Attackers are getting smarter, automating faster, and using better obfuscation.
But defenses are evolving too — and the next decade of Sybil resistance will feel completely different from today.
Let’s break down the three biggest shifts.
1. Off-chain Computation + On-chain ZK Attestation
(Smart security without burning gas)**
Heavy ML does not belong on a blockchain.
It’s slow, expensive, and inefficient.
The future looks like this:
Model runs off-chain (fast, cheap)
Model outputs a verdict (score, risk, or cluster ID)
A ZK proof is generated confirming:
“This verdict came from this model, without revealing user data.”
On-chain contracts simply verify the proof.
It’s like saying:
“Trust my computation, not my server.”
Zero-knowledge makes this possible.
This is the direction every serious protocol will move toward.
2. Modular Detection Layers
(Unplug the old defense, plug in a new one — without downtime)**
Right now, Sybil detectors are often “all or nothing.”
If your model changes, your whole system needs a rewrite.
The future is modular:
You can replace any module without touching everything else.
This also means:
You can run multiple detectors in parallel
You can soft-launch new models
You can keep evolving without disrupting rewards
Modularity makes Sybil resistance future-proof.
3. Economic-First Incentive Engineering
(Make attacks unprofitable, not just detectable)**
The strongest Sybil defense is not perfect detection.
It’s making the attack financially pointless.
If the cost to operate 1 fake wallet is higher than the expected reward,
the Sybil economy collapses.
This leads to powerful new design patterns:
dynamic rewards
delayed payout windows
trust multipliers
bond-based claiming
epochs that favor consistent participation over bursts
You’re not trying to catch every attacker.
You’re trying to make them say:
“This is not worth the effort.”
That’s how you win.
Final Thoughts
Sybil attacks aren’t going away — they’re evolving.
But so are we.
From advanced graph forensics to L2-optimized smart contracts, from CNN-BiLSTM scoring engines to ZK attestations…
Web3 finally has the tools to fight back at scale.
Sybil resistance isn’t a feature.
It’s not even security.
It’s the foundation of real, verifiable growth in decentralized ecosystems.
Protocols that invest in this today won’t just survive the next era of Web3 —
they’ll define it.
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