AI MEV Protection: Stop Bots from Stealing Your Lunch Money (And Your Transactions)

// Victim's transaction (pending in mempool)
function swapTokens(uint256 amountIn) public {
    // User tries to swap 1000 USDC for ETH
    uniswapRouter.swapExactTokensForTokens(
        1000 * 10**6,  // 1000 USDC
        0,             // Accept any amount of ETH
        path,
        msg.sender,
        block.timestamp + 300
    );
}

// MEV Bot's frontrunning transaction (higher gas price)
function frontrunTrade() external {
    // Bot buys ETH first, raising the price
    uniswapRouter.swapExactTokensForTokens(
        50000 * 10**6,  // 50,000 USDC
        0,
        path,
        address(this),
        block.timestamp + 300
    );
    
    // Victim's transaction executes at worse price
    // Bot then sells ETH back for profit
}

import pandas as pd
import numpy as np
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler

class MEVDetector:
    def __init__(self):
        self.scaler = StandardScaler()
        self.anomaly_detector = IsolationForest(
            contamination=0.1,  # Expect 10% MEV transactions
            random_state=42
        )
        
    def extract_features(self, transactions):
        """Extract MEV-indicating features from transactions"""
        features = []
        
        for tx in transactions:
            feature_vector = [
                tx['gas_price'],              # High gas = frontrunning attempt
                tx['value'],                  # Transaction value
                tx['gas_limit'],              # Gas limit
                self.calculate_slippage_tolerance(tx),
                self.get_mempool_position(tx),
                self.calculate_profit_potential(tx),
                len(tx['calldata']),          # Complex calls = MEV opportunity
                self.get_similar_tx_count(tx) # Clustering indicator
            ]
            features.append(feature_vector)
            
        return np.array(features)
    
    def calculate_slippage_tolerance(self, tx):
        """Low slippage tolerance = MEV vulnerability"""
        # Parse transaction data for slippage parameters
        if 'slippage' in tx:
            return tx['slippage']
        return 0  # Default if not specified
    
    def get_mempool_position(self, tx):
        """Transaction position in mempool queue"""
        # Higher position = later execution
        return tx.get('mempool_position', 0)
    
    def calculate_profit_potential(self, tx):
        """Estimate arbitrage profit potential"""
        # Calculate price differences across DEXes
        # This is a simplified version
        return tx.get('arbitrage_opportunity', 0)
    
    def get_similar_tx_count(self, tx):
        """Count similar transactions (sandwich attack indicator)"""
        # Count transactions with similar parameters
        return tx.get('similar_count', 0)

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

class MEVClassifier:
    def __init__(self):
        self.detector = MEVDetector()
        self.classifier = RandomForestClassifier(
            n_estimators=100,
            max_depth=10,
            random_state=42
        )
        
    def train(self, historical_transactions, labels):
        """Train model on historical MEV data"""
        # Extract features from historical transactions
        X = self.detector.extract_features(historical_transactions)
        
        # Normalize features
        X_scaled = self.detector.scaler.fit_transform(X)
        
        # Split data for training and validation
        X_train, X_test, y_train, y_test = train_test_split(
            X_scaled, labels, test_size=0.2, random_state=42
        )
        
        # Train the classifier
        self.classifier.fit(X_train, y_train)
        
        # Evaluate performance
        y_pred = self.classifier.predict(X_test)
        print(classification_report(y_test, y_pred))
        
        return self.classifier.score(X_test, y_test)
    
    def predict_mev_risk(self, transaction):
        """Predict if transaction is likely MEV target"""
        features = self.detector.extract_features([transaction])
        features_scaled = self.detector.scaler.transform(features)
        
        # Get prediction probability
        mev_probability = self.classifier.predict_proba(features_scaled)[0][1]
        
        return {
            'is_mev_risk': mev_probability > 0.7,
            'confidence': mev_probability,
            'risk_level': self.categorize_risk(mev_probability)
        }
    
    def categorize_risk(self, probability):
        """Categorize MEV risk level"""
        if probability > 0.8:
            return "HIGH"
        elif probability > 0.5:
            return "MEDIUM"
        else:
            return "LOW"

import asyncio
from web3 import Web3
from concurrent.futures import ThreadPoolExecutor

class MempoolMonitor:
    def __init__(self, web3_provider, mev_classifier):
        self.w3 = Web3(web3_provider)
        self.classifier = mev_classifier
        self.protection_strategies = MEVProtectionStrategies()
        
    async def monitor_pending_transactions(self):
        """Monitor pending transactions for MEV opportunities"""
        
        def handle_pending_tx(tx_hash):
            try:
                # Get transaction details
                tx = self.w3.eth.get_transaction(tx_hash)
                
                # Analyze for MEV risk
                risk_analysis = self.classifier.predict_mev_risk(tx)
                
                if risk_analysis['is_mev_risk']:
                    print(f"🚨 MEV Risk Detected: {tx_hash.hex()}")
                    print(f"Confidence: {risk_analysis['confidence']:.2%}")
                    
                    # Trigger protection mechanism
                    self.trigger_protection(tx, risk_analysis)
                    
            except Exception as e:
                print(f"Error analyzing transaction: {e}")
        
        # Set up pending transaction filter
        pending_filter = self.w3.eth.filter('pending')
        
        # Use thread pool for concurrent processing
        with ThreadPoolExecutor(max_workers=10) as executor:
            while True:
                try:
                    # Get new pending transactions
                    for tx_hash in pending_filter.get_new_entries():
                        # Process transaction in separate thread
                        executor.submit(handle_pending_tx, tx_hash)
                        
                    await asyncio.sleep(0.1)  # Small delay to prevent spam
                    
                except Exception as e:
                    print(f"Monitoring error: {e}")
                    await asyncio.sleep(1)
    
    def trigger_protection(self, vulnerable_tx, risk_analysis):
        """Activate protection mechanisms for vulnerable transaction"""
        
        protection_type = self.determine_protection_strategy(
            vulnerable_tx, 
            risk_analysis
        )
        
        if protection_type == "PRIVATE_MEMPOOL":
            self.protection_strategies.use_private_mempool(vulnerable_tx)
        elif protection_type == "COMMIT_REVEAL":
            self.protection_strategies.use_commit_reveal(vulnerable_tx)
        elif protection_type == "BUNDLE_AUCTION":
            self.protection_strategies.create_bundle_auction(vulnerable_tx)
        
    def determine_protection_strategy(self, tx, risk_analysis):
        """Choose optimal protection strategy based on risk analysis"""
        
        if risk_analysis['risk_level'] == "HIGH":
            return "PRIVATE_MEMPOOL"
        elif tx['value'] > Web3.toWei(10, 'ether'):
            return "BUNDLE_AUCTION"
        else:
            return "COMMIT_REVEAL"

class MEVProtectionStrategies:
    def __init__(self):
        self.flashbots_relay = "https://relay.flashbots.net"
        self.private_mempools = ["Flashbots", "Eden", "1inch"]
        
    def use_private_mempool(self, transaction):
        """Route transaction through private mempool"""
        print("🛡️  Routing through private mempool...")
        
        # Create Flashbots bundle
        bundle = {
            "txs": [transaction],
            "blockNumber": self.get_target_block(),
            "minTimestamp": 0,
            "maxTimestamp": 0
        }
        
        # Submit to Flashbots
        response = self.submit_flashbots_bundle(bundle)
        
        if response['success']:
            print("✅ Transaction protected via private mempool")
        else:
            print("❌ Private mempool protection failed")
            
        return response
    
    def use_commit_reveal(self, transaction):
        """Implement commit-reveal scheme"""
        print("🔐 Using commit-reveal protection...")
        
        # Phase 1: Commit
        commit_hash = Web3.keccak(
            text=f"{transaction['data']}{transaction['nonce']}"
        )
        
        commit_tx = {
            'to': self.commit_contract_address,
            'data': f"0x{commit_hash.hex()}",
            'gas': 50000,
            'gasPrice': transaction['gasPrice']
        }
        
        # Submit commit transaction
        commit_result = self.submit_transaction(commit_tx)
        
        if commit_result['success']:
            # Phase 2: Reveal (after delay)
            asyncio.create_task(
                self.reveal_after_delay(transaction, commit_hash, 30)  # 30 second delay
            )
            
        return commit_result
    
    async def reveal_after_delay(self, original_tx, commit_hash, delay_seconds):
        """Reveal transaction after commit delay"""
        await asyncio.sleep(delay_seconds)
        
        reveal_tx = {
            **original_tx,
            'data': f"{original_tx['data']}{commit_hash.hex()}"
        }
        
        result = self.submit_transaction(reveal_tx)
        print(f"🔓 Transaction revealed: {'✅' if result['success'] else '❌'}")
        
    def create_bundle_auction(self, transaction):
        """Create MEV auction for transaction"""
        print("🏦 Creating bundle auction...")
        
        auction_params = {
            'transaction': transaction,
            'min_tip': Web3.toWei(0.01, 'ether'),
            'duration': 300,  # 5 minute auction
            'protection_fee': 0.05  # 5% protection fee
        }
        
        # Submit to auction platform
        auction_result = self.submit_to_auction(auction_params)
        
        if auction_result['success']:
            print(f"✅ Auction created. ID: {auction_result['auction_id']}")
        
        return auction_result
    
    def submit_flashbots_bundle(self, bundle):
        """Submit bundle to Flashbots relay"""
        # Simplified Flashbots submission
        # In practice, you'd use the official Flashbots SDK
        
        headers = {
            'Content-Type': 'application/json',
            'X-Flashbots-Signature': self.sign_bundle(bundle)
        }
        
        # This is a placeholder - use actual Flashbots API
        return {'success': True, 'bundle_id': 'mock_bundle_123'}
    
    def sign_bundle(self, bundle):
        """Sign bundle for Flashbots submission"""
        # Implement proper bundle signing
        return "0x_mock_signature"
    
    def get_target_block(self):
        """Get optimal target block for bundle inclusion"""
        current_block = Web3().eth.block_number
        return current_block + 1  # Target next block
    
    def submit_transaction(self, tx):
        """Submit transaction with error handling"""
        try:
            # Simulate transaction submission
            return {'success': True, 'tx_hash': '0x_mock_hash'}
        except Exception as e:
            return {'success': False, 'error': str(e)}
    
    def submit_to_auction(self, auction_params):
        """Submit transaction to MEV auction platform"""
        # Placeholder for auction platform integration
        return {'success': True, 'auction_id': 'auction_123'}

import asyncio
import logging
from typing import Dict, List, Optional

class CompleteMEVProtection:
    def __init__(self, config: Dict):
        self.config = config
        self.setup_logging()
        
        # Initialize components
        self.mev_classifier = MEVClassifier()
        self.mempool_monitor = MempoolMonitor(
            config['web3_provider'], 
            self.mev_classifier
        )
        self.protection_strategies = MEVProtectionStrategies()
        
        # Load pre-trained model
        self.load_trained_model()
        
    def setup_logging(self):
        """Configure logging for MEV protection events"""
        logging.basicConfig(
            level=logging.INFO,
            format='%(asctime)s - MEV Protection - %(levelname)s - %(message)s',
            handlers=[
                logging.FileHandler('mev_protection.log'),
                logging.StreamHandler()
            ]
        )
        self.logger = logging.getLogger(__name__)
    
    def load_trained_model(self):
        """Load pre-trained MEV detection model"""
        try:
            # Load historical MEV data for training
            historical_data = self.load_historical_mev_data()
            labels = self.load_mev_labels()
            
            # Train the classifier
            accuracy = self.mev_classifier.train(historical_data, labels)
            self.logger.info(f"MEV classifier trained with {accuracy:.2%} accuracy")
            
        except Exception as e:
            self.logger.error(f"Failed to load MEV model: {e}")
            raise
    
    async def start_protection(self):
        """Start the complete MEV protection system"""
        self.logger.info("🚀 Starting MEV protection system...")
        
        try:
            # Start mempool monitoring
            monitoring_task = asyncio.create_task(
                self.mempool_monitor.monitor_pending_transactions()
            )
            
            # Start metrics collection
            metrics_task = asyncio.create_task(
                self.collect_protection_metrics()
            )
            
            # Wait for both tasks
            await asyncio.gather(monitoring_task, metrics_task)
            
        except KeyboardInterrupt:
            self.logger.info("⏹️  MEV protection system stopped")
        except Exception as e:
            self.logger.error(f"Protection system error: {e}")
            raise
    
    async def collect_protection_metrics(self):
        """Collect and log protection system metrics"""
        while True:
            try:
                metrics = {
                    'transactions_analyzed': self.get_analyzed_count(),
                    'mev_attacks_detected': self.get_detected_count(),
                    'successful_protections': self.get_protected_count(),
                    'false_positive_rate': self.calculate_false_positive_rate()
                }
                
                self.logger.info(f"📊 Protection metrics: {metrics}")
                
                # Export metrics to monitoring system
                self.export_metrics(metrics)
                
                await asyncio.sleep(60)  # Log every minute
                
            except Exception as e:
                self.logger.error(f"Metrics collection error: {e}")
                await asyncio.sleep(60)
    
    def protect_user_transaction(self, user_tx: Dict) -> Dict:
        """Main API for protecting a user transaction"""
        
        self.logger.info(f"🔍 Analyzing transaction: {user_tx.get('hash', 'N/A')}")
        
        # Analyze MEV risk
        risk_analysis = self.mev_classifier.predict_mev_risk(user_tx)
        
        protection_result = {
            'original_transaction': user_tx,
            'risk_analysis': risk_analysis,
            'protection_applied': None,
            'success': False
        }
        
        if risk_analysis['is_mev_risk']:
            self.logger.warning(
                f"⚠️  High MEV risk detected ({risk_analysis['confidence']:.2%})"
            )
            
            # Apply protection
            protection_type = self.mempool_monitor.determine_protection_strategy(
                user_tx, risk_analysis
            )
            
            if protection_type == "PRIVATE_MEMPOOL":
                result = self.protection_strategies.use_private_mempool(user_tx)
            elif protection_type == "COMMIT_REVEAL":
                result = self.protection_strategies.use_commit_reveal(user_tx)
            elif protection_type == "BUNDLE_AUCTION":
                result = self.protection_strategies.create_bundle_auction(user_tx)
            
            protection_result['protection_applied'] = protection_type
            protection_result['success'] = result.get('success', False)
            
        else:
            self.logger.info("✅ Low MEV risk - no protection needed")
            protection_result['success'] = True
        
        return protection_result
    
    # Utility methods for metrics
    def get_analyzed_count(self): return 0  # Implement actual counting
    def get_detected_count(self): return 0
    def get_protected_count(self): return 0
    def calculate_false_positive_rate(self): return 0.0
    def export_metrics(self, metrics): pass
    def load_historical_mev_data(self): return []
    def load_mev_labels(self): return []

# Usage example
async def main():
    config = {
        'web3_provider': 'wss://mainnet.infura.io/ws/v3/YOUR_KEY',
        'flashbots_signer_key': 'your_private_key',
        'protection_enabled': True
    }
    
    # Initialize protection system
    mev_protection = CompleteMEVProtection(config)
    
    # Start monitoring
    await mev_protection.start_protection()

if __name__ == "__main__":
    asyncio.run(main())

class OptimizedMEVDetector:
    def __init__(self):
        # Use lightweight models for real-time detection
        from sklearn.linear_model import SGDClassifier
        
        self.fast_classifier = SGDClassifier(
            loss='log',  # Logistic regression
            learning_rate='constant',
            eta0=0.01,
            random_state=42
        )
        
        # Pre-compute common patterns
        self.pattern_cache = {}
        self.feature_cache = {}
        
    def fast_feature_extraction(self, tx):
        """Optimized feature extraction for real-time detection"""
        
        # Cache key for feature reuse
        cache_key = f"{tx['to']}_{tx['data'][:10]}"
        
        if cache_key in self.feature_cache:
            return self.feature_cache[cache_key]
        
        # Extract only essential features for speed
        features = np.array([
            tx['gasPrice'] / 1e9,        # Normalize gas price
            len(tx['data']) / 1000,      # Normalize data length
            tx.get('value', 0) / 1e18,   # Normalize value
            hash(tx['to']) % 1000        # Address hash feature
        ])
        
        # Cache for reuse
        self.feature_cache[cache_key] = features
        return features
    
    def batch_predict(self, transactions):
        """Process multiple transactions in batch for efficiency"""
        features_batch = np.array([
            self.fast_feature_extraction(tx) for tx in transactions
        ])
        
        # Batch prediction is faster than individual predictions
        predictions = self.fast_classifier.predict_proba(features_batch)
        
        return [
            {'mev_risk': pred[1] > 0.7, 'confidence': pred[1]}
            for pred in predictions
        ]

import redis
from multiprocessing import Process, Queue

class DistributedMEVProtection:
    def __init__(self, node_id, redis_config):
        self.node_id = node_id
        self.redis_client = redis.Redis(**redis_config)
        self.local_detector = OptimizedMEVDetector()
        
    def start_protection_node(self):
        """Start a protection node in the distributed network"""
        
        # Subscribe to transaction stream
        pubsub = self.redis_client.pubsub()
        pubsub.subscribe('pending_transactions')
        
        print(f"🌐 Node {self.node_id} started - listening for transactions...")
        
        for message in pubsub.listen():
            if message['type'] == 'message':
                try:
                    tx_data = json.loads(message['data'])
                    
                    # Process transaction
                    result = self.analyze_transaction(tx_data)
                    
                    if result['mev_risk']:
                        # Alert network about MEV risk
                        self.broadcast_mev_alert(tx_data, result)
                        
                except Exception as e:
                    print(f"Node {self.node_id} error: {e}")
    
    def broadcast_mev_alert(self, tx, risk_result):
        """Broadcast MEV risk alert to protection network"""
        
        alert = {
            'node_id': self.node_id,
            'timestamp': time.time(),
            'transaction': tx,
            'risk_analysis': risk_result,
            'protection_needed': True
        }
        
        # Publish to network
        self.redis_client.publish('mev_alerts', json.dumps(alert))
        
        print(f"🚨 Node {self.node_id} broadcast MEV alert")

# Deploy multiple protection nodes
def deploy_protection_network():
    redis_config = {'host': 'localhost', 'port': 6379, 'db': 0}
    
    # Start multiple protection nodes
    nodes = []
    for i in range(4):  # 4 protection nodes
        node = Process(
            target=lambda: DistributedMEVProtection(f"node_{i}", redis_config).start_protection_node()
        )
        node.start()
        nodes.append(node)
    
    return nodes

import unittest
from unittest.mock import Mock, patch

class TestMEVProtection(unittest.TestCase):
    def setUp(self):
        """Set up test environment"""
        self.mev_classifier = MEVClassifier()
        self.protection_system = CompleteMEVProtection({
            'web3_provider': 'mock_provider',
            'test_mode': True
        })
        
    def test_frontrunning_detection(self):
        """Test detection of frontrunning attacks"""
        
        # Create mock frontrunning transaction
        frontrun_tx = {
            'gasPrice': Web3.toWei(100, 'gwei'),  # High gas price
            'value': Web3.toWei(1, 'ether'),
            'data': '0x38ed1739',  # Uniswap swap function
            'to': '0x7a250d5630B4cF539739dF2C5dAcb4c659F2488D'  # Uniswap router
        }
        
        risk_analysis = self.mev_classifier.predict_mev_risk(frontrun_tx)
        
        self.assertTrue(risk_analysis['is_mev_risk'])
        self.assertGreater(risk_analysis['confidence'], 0.8)
        
    def test_sandwich_attack_detection(self):
        """Test detection of sandwich attacks"""
        
        # Create sequence of sandwich attack transactions
        transactions = [
            # Front transaction (buy)
            {
                'gasPrice': Web3.toWei(50, 'gwei'),
                'value': Web3.toWei(10, 'ether'),
                'data': '0x38ed1739',  # Swap function
                'timestamp': 1640995200
            },
            # Victim transaction
            {
                'gasPrice': Web3.toWei(20, 'gwei'),
                'value': Web3.toWei(1, 'ether'),
                'data': '0x38ed1739',
                'timestamp': 1640995201
            },
            # Back transaction (sell)
            {
                'gasPrice': Web3.toWei(50, 'gwei'),
                'value': Web3.toWei(10, 'ether'),
                'data': '0x38ed1739',
                'timestamp': 1640995202
            }
        ]
        
        # Detect sandwich pattern
        sandwich_detected = self.detect_sandwich_pattern(transactions)
        self.assertTrue(sandwich_detected)
        
    def test_private_mempool_protection(self):
        """Test private mempool protection mechanism"""
        
        with patch('requests.post') as mock_post:
            mock_post.return_value.json.return_value = {'success': True}
            
            vulnerable_tx = {
                'gasPrice': Web3.toWei(30, 'gwei'),
                'value': Web3.toWei(5, 'ether'),
                'data': '0x38ed1739'
            }
            
            result = self.protection_system.protection_strategies.use_private_mempool(vulnerable_tx)
            
            self.assertTrue(result['success'])
            mock_post.assert_called_once()
    
    def test_false_positive_rate(self):
        """Test system doesn't flag normal transactions"""
        
        normal_transactions = [
            {
                'gasPrice': Web3.toWei(20, 'gwei'),  # Normal gas price
                'value': Web3.toWei(0.1, 'ether'),   # Small value
                'data': '0xa9059cbb',                 # Simple transfer
                'to': '0xA0b86a33E6417a55CAD25A69F37b6AbD3476eB8a'
            }
            # Add more normal transactions...
        ]
        
        false_positives = 0
        for tx in normal_transactions:
            risk_analysis = self.mev_classifier.predict_mev_risk(tx)
            if risk_analysis['is_mev_risk']:
                false_positives += 1
        
        false_positive_rate = false_positives / len(normal_transactions)
        self.assertLess(false_positive_rate, 0.1)  # Less than 10% false positives
        
    def detect_sandwich_pattern(self, transactions):
        """Helper method to detect sandwich attack patterns"""
        # Simplified sandwich detection logic
        if len(transactions) >= 3:
            first_tx = transactions[0]
            last_tx = transactions[-1]
            
            # Check if first and last transactions are from same address with opposite operations
            return (first_tx['gasPrice'] == last_tx['gasPrice'] and
                   abs(first_tx['timestamp'] - last_tx['timestamp']) < 10)
        
        return False

# Run tests
if __name__ == '__main__':
    unittest.main()

import time
import statistics
from concurrent.futures import ThreadPoolExecutor

class MEVProtectionBenchmark:
    def __init__(self, protection_system):
        self.protection_system = protection_system
        
    def benchmark_detection_speed(self, num_transactions=1000):
        """Benchmark MEV detection speed"""
        
        # Generate test transactions
        test_transactions = self.generate_test_transactions(num_transactions)
        
        detection_times = []
        
        for tx in test_transactions:
            start_time = time.perf_counter()
            
            # Perform MEV detection
            risk_analysis = self.protection_system.mev_classifier.predict_mev_risk(tx)
            
            end_time = time.perf_counter()
            detection_times.append(end_time - start_time)
        
        # Calculate statistics
        avg_time = statistics.mean(detection_times)
        median_time = statistics.median(detection_times)
        p95_time = statistics.quantiles(detection_times, n=20)[18]  # 95th percentile
        
        print(f"📊 MEV Detection Performance:")
        print(f"   Average: {avg_time*1000:.2f}ms")
        print(f"   Median:  {median_time*1000:.2f}ms") 
        print(f"   95th %:  {p95_time*1000:.2f}ms")
        print(f"   Throughput: {1/avg_time:.0f} tx/second")
        
        return {
            'average_time': avg_time,
            'median_time': median_time,
            'p95_time': p95_time,
            'throughput': 1/avg_time
        }
    
    def benchmark_concurrent_processing(self, num_workers=10, transactions_per_worker=100):
        """Benchmark concurrent transaction processing"""
        
        def process_batch(batch_transactions):
            batch_times = []
            for tx in batch_transactions:
                start = time.perf_counter()
                self.protection_system.mev_classifier.predict_mev_risk(tx)
                batch_times.append(time.perf_counter() - start)
            return batch_times
        
        # Generate transaction batches
        all_transactions = self.generate_test_transactions(num_workers * transactions_per_worker)
        batches = [
            all_transactions[i:i+transactions_per_worker] 
            for i in range(0, len(all_transactions), transactions_per_worker)
        ]
        
        start_time = time.perf_counter()
        
        # Process batches concurrently
        with ThreadPoolExecutor(max_workers=num_workers) as executor:
            batch_results = list(executor.map(process_batch, batches))
        
        total_time = time.perf_counter() - start_time
        
        # Flatten results
        all_times = [time for batch in batch_results for time in batch]
        
        print(f"🚀 Concurrent Processing Performance:")
        print(f"   Workers: {num_workers}")
        print(f"   Total transactions: {len(all_transactions)}")
        print(f"   Total time: {total_time:.2f}s")
        print(f"   Concurrent throughput: {len(all_transactions)/total_time:.0f} tx/second")
        
        return {
            'total_time': total_time,
            'concurrent_throughput': len(all_transactions)/total_time,
            'individual_times': all_times
        }
    
    def generate_test_transactions(self, count):
        """Generate realistic test transactions for benchmarking"""
        import random
        
        transactions = []
        for i in range(count):
            tx = {
                'gasPrice': random.randint(10, 200) * 10**9,  # 10-200 Gwei
                'value': random.randint(0, 100) * 10**18,     # 0-100 ETH
                'data': f"0x{''.join(random.choices('0123456789abcdef', k=8))}",
                'to': f"0x{''.join(random.choices('0123456789abcdef', k=40))}",
                'mempool_position': random.randint(1, 1000),
                'similar_count': random.randint(0, 5)
            }
            transactions.append(tx)
        
        return transactions

# Run benchmarks
benchmark = MEVProtectionBenchmark(mev_protection_system)
benchmark.benchmark_detection_speed()
benchmark.benchmark_concurrent_processing()

# docker-compose.yml
version: '3.8'

services:
  mev-protection:
    build: .
    environment:
      - WEB3_PROVIDER_URL=${WEB3_PROVIDER_URL}
      - REDIS_URL=redis://redis:6379
      - FLASHBOTS_PRIVATE_KEY=${FLASHBOTS_PRIVATE_KEY}
      - LOG_LEVEL=INFO
    depends_on:
      - redis
      - prometheus
    networks:
      - mev-network
    restart: unless-stopped
    
  redis:
    image: redis:7-alpine
    networks:
      - mev-network
    volumes:
      - redis-data:/data
    restart: unless-stopped
    
  prometheus:
    image: prom/prometheus:latest
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
      - prometheus-data:/prometheus
    networks:
      - mev-network
    restart: unless-stopped
    
  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=admin
    volumes:
      - grafana-data:/var/lib/grafana
    networks:
      - mev-network
    restart: unless-stopped

volumes:
  redis-data:
  prometheus-data:
  grafana-data:

networks:
  mev-network:
    driver: bridge

from prometheus_client import Counter, Histogram, Gauge, start_http_server
import time

# Prometheus metrics
MEV_DETECTIONS = Counter('mev_detections_total', 'Total MEV attacks detected')
PROTECTION_APPLIED = Counter('mev_protections_applied_total', 'Total protections applied', ['type'])
DETECTION_LATENCY = Histogram('mev_detection_latency_seconds', 'MEV detection latency')
FALSE_POSITIVES = Counter('mev_false_positives_total', 'False positive detections')
SYSTEM_HEALTH = Gauge('mev_system_health', 'System health score (0-100)')

class MonitoredMEVProtection(CompleteMEVProtection):
    def __init__(self, config):
        super().__init__(config)
        
        # Start Prometheus metrics server
        start_http_server(8000)
        
    @DETECTION_LATENCY.time()
    def analyze_transaction_with_metrics(self, transaction):
        """Analyze transaction with performance monitoring"""
        
        start_time = time.time()
        
        try:
            # Perform MEV analysis
            risk_analysis = self.mev_classifier.predict_mev_risk(transaction)
            
            if risk_analysis['is_mev_risk']:
                MEV_DETECTIONS.inc()
                
                # Apply protection and track type
                protection_type = self.determine_protection_strategy(transaction, risk_analysis)
                PROTECTION_APPLIED.labels(type=protection_type).inc()
                
            # Update system health
            self.update_system_health()
            
            return risk_analysis
            
        except Exception as e:
            self.logger.error(f"Analysis error: {e}")
            raise
    
    def update_system_health(self):
        """Calculate and update system health score"""
        
        # Health factors (simplified)
        factors = {
            'detection_accuracy': 0.95,      # 95% accuracy
            'response_time': 0.98,           # 98% under target latency
            'system_uptime': 0.999,          # 99.9% uptime
            'protection_success': 0.92       # 92% successful protections
        }
        
        # Weighted health score
        health_score = sum(factors.values()) / len(factors) * 100
        SYSTEM_HEALTH.set(health_score)
    
    def setup_alerting(self):
        """Configure alerting rules for critical events"""
        
        alerting_rules = {
            'high_mev_activity': {
                'condition': 'mev_detections_rate > 100/hour',
                'severity': 'warning',
                'action': self.handle_high_mev_activity
            },
            'protection_failures': {
                'condition': 'protection_failure_rate > 10%', 
                'severity': 'critical',
                'action': self.handle_protection_failures
            },
            'system_degradation': {
                'condition': 'mev_system_health < 80',
                'severity': 'critical', 
                'action': self.handle_system_degradation
            }
        }
        
        return alerting_rules
    
    def handle_high_mev_activity(self):
        """Handle alert for high MEV activity"""
        self.logger.warning("🚨 High MEV activity detected - scaling protection")
        # Scale up protection nodes
        # Increase monitoring frequency
        # Alert operations team
        
    def handle_protection_failures(self):
        """Handle alert for protection system failures"""
        self.logger.critical("💥 Protection system failures detected")
        # Failover to backup systems
        # Emergency notification
        # Automatic recovery procedures
        
    def handle_system_degradation(self):
        """Handle alert for system health degradation"""
        self.logger.critical("⚠️ System health degraded")
        # Run diagnostic checks
        # Restart unhealthy components
        # Scale infrastructure if needed

import secrets
import hashlib
from cryptography.fernet import Fernet

class SecureMEVProtection:
    def __init__(self):
        # Generate secure encryption key
        self.encryption_key = Fernet.generate_key()
        self.cipher = Fernet(self.encryption_key)
        
        # Rate limiting for API endpoints
        self.rate_limiter = self.setup_rate_limiting()
        
    def setup_rate_limiting(self):
        """Configure rate limiting to prevent abuse"""
        from collections import defaultdict
        import time
        
        class RateLimiter:
            def __init__(self, max_requests=100, time_window=3600):
                self.max_requests = max_requests
                self.time_window = time_window
                self.requests = defaultdict(list)
            
            def is_allowed(self, client_id):
                now = time.time()
                client_requests = self.requests[client_id]
                
                # Remove old requests outside time window
                client_requests[:] = [req for req in client_requests 
                                    if now - req < self.time_window]
                
                # Check if under limit
                if len(client_requests) < self.max_requests:
                    client_requests.append(now)
                    return True
                
                return False
        
        return RateLimiter()
    
    def secure_transaction_submission(self, transaction, client_id):
        """Securely handle transaction submission with validation"""
        
        # Rate limiting check
        if not self.rate_limiter.is_allowed(client_id):
            raise Exception("Rate limit exceeded")
        
        # Input validation
        if not self.validate_transaction(transaction):
            raise Exception("Invalid transaction format")
        
        # Encrypt sensitive transaction data
        encrypted_tx = self.encrypt_transaction_data(transaction)
        
        # Add integrity check
        tx_hash = self.calculate_transaction_hash(transaction)
        
        return {
            'encrypted_transaction': encrypted_tx,
            'integrity_hash': tx_hash,
            'timestamp': time.time()
        }
    
    def validate_transaction(self, transaction):
        """Validate transaction format and content"""
        
        required_fields = ['to', 'data', 'gasPrice', 'gas']
        
        # Check required fields
        for field in required_fields:
            if field not in transaction:
                return False
        
        # Validate addresses
        if not Web3.isAddress(transaction['to']):
            return False
        
        # Validate gas price range (prevent extremely high gas prices)
        max_gas_price = Web3.toWei(1000, 'gwei')  # 1000 Gwei max
        if transaction['gasPrice'] > max_gas_price:
            return False
        
        return True
    
    def encrypt_transaction_data(self, transaction):
        """Encrypt sensitive transaction data"""
        
        # Serialize transaction
        tx_data = json.dumps(transaction, sort_keys=True)
        
        # Encrypt with Fernet
        encrypted_data = self.cipher.encrypt(tx_data.encode())
        
        return encrypted_data.decode()
    
    def calculate_transaction_hash(self, transaction):
        """Calculate integrity hash for transaction"""
        
        # Create deterministic string representation
        tx_string = json.dumps(transaction, sort_keys=True)
        
        # Calculate SHA-256 hash
        hash_object = hashlib.sha256(tx_string.encode())
        return hash_object.hexdigest()
    
    def secure_private_key_management(self):
        """Implement secure private key handling"""
        
        import os
        from cryptography.hazmat.primitives import hashes
        from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
        
        # Use environment variables for sensitive data
        encrypted_key = os.environ.get('ENCRYPTED_PRIVATE_KEY')
        if not encrypted_key:
            raise Exception("Private key not found in environment")
        
        # Derive decryption key from password
        password = os.environ.get('KEY_PASSWORD', '').encode()
        salt = os.environ.get('KEY_SALT', '').encode()
        
        kdf = PBKDF2HMAC(
            algorithm=hashes.SHA256(),
            length=32,
            salt=salt,
            iterations=100000,  # High iteration count for security
        )
        
        key = base64.urlsafe_b64encode(kdf.derive(password))
        cipher = Fernet(key)
        
        # Decrypt private key
        private_key = cipher.decrypt(encrypted_key.encode()).decode()
        
        return private_key

// GasOptimizedMEVProtection.sol
pragma solidity ^0.8.19;

contract GasOptimizedMEVProtection {
    
    // Use packed structs to save storage
    struct ProtectionData {
        uint128 gasPrice;      // 16 bytes
        uint64 timestamp;      // 8 bytes  
        uint32 nonce;          // 4 bytes
        uint32 blockNumber;    // 4 bytes
        // Total: 32 bytes (1 storage slot)
    }
    
    mapping(bytes32 => ProtectionData) private protections;
    
    // Use events for off-chain monitoring (cheaper than storage)
    event MEVProtectionApplied(
        bytes32 indexed txHash,
        uint256 gasPrice,
        uint256 timestamp
    );
    
    // Batch multiple protections to amortize gas costs
    function batchProtectTransactions(
        bytes32[] calldata txHashes,
        ProtectionData[] calldata protectionData
    ) external {
        require(txHashes.length == protectionData.length, "Mismatched arrays");
        
        for (uint256 i = 0; i < txHashes.length;) {
            protections[txHashes[i]] = protectionData[i];
            
            emit MEVProtectionApplied(
                txHashes[i],
                protectionData[i].gasPrice,
                protectionData[i].timestamp
            );
            
            unchecked { ++i; } // Gas optimization: skip overflow check
        }
    }
    
    // Use assembly for maximum gas efficiency in critical functions
    function fastHashCheck(bytes32 txHash) external view returns (bool) {
        assembly {
            // Load protection data from storage
            let slot := protections.slot
            let hash := txHash
            
            // Calculate storage slot for mapping
            mstore(0x00, hash)
            mstore(0x20, slot)
            let storageSlot := keccak256(0x00, 0x40)
            
            // Load and check if protection exists
            let data := sload(storageSlot)
            let exists := gt(data, 0)
            
            // Return result
            mstore(0x00, exists)
            return(0x00, 0x20)
        }
    }
    
    // Use CREATE2 for deterministic protection contract addresses
    function deployProtectionContract(
        bytes32 salt,
        bytes memory bytecode
    ) external returns (address) {
        address addr;
        
        assembly {
            addr := create2(
                0,                    // value
                add(bytecode, 0x20),  // code
                mload(bytecode),      // code length
                salt                  // salt
            )
        }
        
        require(addr != address(0), "Deployment failed");
        return addr;
    }
}