Live - 2.4M transactions monitored today

Blockchain security,
powered by AI
in real time.

Neurablock neural engine monitors every transaction, smart contract call, and wallet interaction - detecting and blocking attacks before they execute.

8,431TX / sec
99.97%Detection rate
<1msLatency
neurablock / live-monitor
PROTECTED
AI Architecture

How the model works

Five specialized neural modules process each transaction simultaneously, each trained on 50M+ historical attacks.

Live Simulation

Watch a flash loan
attack get stopped

A real-time simulation of how Neurablock detects and neutralizes a flash loan reentrancy attack.

Transaction Flow - Ethereum Mainnet Block #21,847,201
Protocol nodes
Attacker wallet
Blocked / safe
Flagged
Capabilities

Everything you need
to stay protected

Mempool Surveillance

Scans the full transaction mempool before mining, catching flash loans, sandwich attacks, and front-running bots before they execute.

Smart Contract Analyzer

Neural bytecode decompilation detects reentrancy, overflow, and proxy exploits across any EVM-compatible contract instantly.

Behavioral AI Engine

Detects zero-day attacks by modeling anomalies across wallet clusters, liquidity pools, and protocol interactions in real time.

Multi-Chain Coverage

Ethereum, Solana, BNB Chain, Polygon, Arbitrum, Avalanche - monitored through one integration with cross-chain bridge protection.

Automated Response

Auto-pause contracts, revoke approvals, or trigger circuit breakers - responding in microseconds, not minutes.

Forensic Audit Trail

Immutable cryptographic logs for compliance, incident response, and insurance documentation.

Research

Enhancing Blockchain Contract
Security: A Machine Learning
Approach to Opcode Analysis

A technical whitepaper by Neurablock exploring how neural opcode analysis detects vulnerabilities with higher precision than traditional audit methods.

Abstract

A pipeline trained on 2.3M annotated EVM bytecode sequences, achieving 99.4% precision in vulnerability classification across reentrancy, integer overflow, and delegatecall exploits.

Methodology

Opcode-level feature extraction feeds a Graph Neural Network with 847 learned representations per contract. Ensemble classifiers score threat probability in under 0.4ms.

Results

Across 6 EVM-compatible chains, the model blocked 847K+ attacks and secured $4.2B in assets. False positive rate below 0.03% across 180-day deployment.

Key Findings

Opcode-level analysis outperforms source-code static analysis by 34% in zero-day detection. Generalizes across Solidity versions and unverified contracts.

Whitepaper - 2026

Download the full research paper

24 pages including dataset methodology, model architecture diagrams, and benchmark comparisons.

Request Whitepaper
Contact

Get in touch

Questions about deployment, pricing, or the whitepaper? Send us a message and we will reply within 24 hours.

Email
hello@neurablock.ai
Response time
Within 24 hours
Security
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