Skip to main content Accueil Créateurs xspoonai spoon-awesome-skill mempool-intrusion-detection-system
mempool-intrusion-detection-system Real-time blockchain intrusion detection system that monitors mempool for exploit attempts and automatically executes defensive responses using ML classification and gas-optimized front-running
Aller à l'installation Skills Marketplace Découvrez et explorez les compétences IA créées par la communauté.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Afficher les détails du prompt Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
npx skills add https://github.com/XSpoonAi/spoon-awesome-skill --skill mempool-intrusion-detection-systemLa commande reste sur une seule ligne. Faites défiler horizontalement pour la vérifier avant de la copier.
Vous préférez une copie locale ? Téléchargez les fichiers actuellement disponibles dans SkillsMP.
Télécharger Zip Téléchargement... CRITICAL: Skill composition engine for automatic workflow generation from skill metadata. Triggers: compose skills, orchestrate, workflow, combine skills, skill pipeline, multi-skill, 技能组合, 工作流编排, 技能发现, 自动编排, skill discovery, semantic matching, StateGraph generation
Métiers associés SOC
Basé sur la classification professionnelle SOC
Explorateur de fichiers
7 fichiers name Mempool Intrusion Detection System category Web3 Data Intelligence subcategory Security Analysis description Real-time blockchain intrusion detection system that monitors mempool for exploit attempts and automatically executes defensive responses using ML classification and gas-optimized front-running tags ["ids","intrusion-detection","mempool-monitoring","exploit-detection","machine-learning","automated-defense","front-running","security","real-time","websocket"] difficulty advanced status production version 1.0.0 activation_triggers ["monitor mempool","detect blockchain exploits","intrusion detection","automated smart contract defense","front-run attacker","pause contract on threat","real-time threat detection"] parameters {"config":{"description":"IDS configuration with network endpoints, monitored contracts, and thresholds","required":true,"example":"IDSConfig(websocket_url='wss://...', monitored_contracts=[...])"},"operational_mode":{"description":"IDS mode (monitor_only, alert_only, active_defense)","required":true,"example":"IDSMode.ACTIVE_DEFENSE"},"classification_threshold":{"description":"Minimum confidence score for threat classification","required":false,"default":0.7},"threat_level_threshold":{"description":"Minimum threat level for automated defense","required":false,"default":"ThreatLevel.HIGH"}} requirements {"python":">=3.8","packages":["web3>=6.0.0","scikit-learn>=1.3.0","numpy>=1.24.0","aiohttp>=3.9.0","eth-account>=0.9.0"],"external":["Ethereum WebSocket endpoint","Ethereum RPC access (archive node recommended)","Defender account with pause permissions"]}
Mempool Intrusion Detection System
##Overview
Real-time blockchain intrusion detection system that monitors the Ethereum mempool for exploit attempts, analyzes transaction payloads using machine learning, and automatically executes defensive responses by front-running attackers with gas-optimized pause transactions.
Analysis Type : Real-Time Intrusion Detection + Automated ResponseApproach : Signature-Based + ML Classification + Active DefenseTarget : Ethereum & EVM-Compatible ChainsStatus : Production Ready
Key Capabilities
1. Mempool Monitoring (mempool_monitor.py)
✅ WebSocket-based real-time transaction monitoring
✅ Automatic reconnection with exponential backoff
✅ Contract address filtering
✅ Transaction decoding and function identification
✅ Event-driven callback architecture
✅ Connection health monitoring
2. Payload Analysis (payload_analyzer.py)
✅ 22-dimensional feature extraction
✅ Function selector entropy calculation
✅ Parameter pattern detection (zero addresses, max values)
✅ Gas price anomaly detection
✅ Reentrancy pattern matching
✅ Flash loan signature detection
✅ Price manipulation pattern identification
3. ML Classification (exploit_classifier.py)
✅ Random Forest and Gradient Boosting models
✅ Confidence scoring and threat level classification
✅ Feature importance analysis
✅ Explainable predictions
✅ Threshold tuning for false positive control
✅ Synthetic training data generation
4. Automated Defense (defense_executor.py)
✅ Gas-optimized front-running strategy
✅ Automated pause contract transactions
✅ Dynamic gas price calculation (EIP-1559)
✅ Transaction confirmation monitoring
✅ Retry logic with escalating gas prices
✅ Multi-strategy defense support
5. IDS Engine (ids_engine.py)
✅ Real-time orchestration of all components
✅ Configurable operational modes (monitor/alert/active defense)
✅ Alert system with webhooks
✅ Comprehensive logging and statistics
✅ Detection export and reporting
Components
mempool_monitor.py (550 lines) Purpose : Real-time WebSocket monitoring of pending transactions
MempoolMonitor - WebSocket connection managerPendingTransaction - Transaction data structureMonitoringStats - Statistics tracker async def connect ()
async def subscribe_pending_transactions ()
async def process_transaction (tx_hash )
_parse_transaction(tx)
_decode_function_call(tx)
async def reconnect ()
payload_analyzer.py (650 lines) Purpose : Transaction payload analysis and feature extraction
PayloadAnalyzer - Feature extraction enginePayloadFeatures - 22D feature vectorExploitPattern - Known exploit signatures analyze(tx_hash, from_addr, to_addr, value, gas_price, input_data, nonce, timestamp)
_calculate_entropy(hex_string)
_analyze_parameters(input_data)
_calculate_gas_percentile(gas_price)
_match_reentrancy_pattern()
_match_flash_loan_pattern()
_match_price_manipulation_pattern()
to_feature_vector()
exploit_classifier.py (600 lines) Purpose : ML-based exploit classification
ExploitClassifier - Ensemble ML modelClassificationResult - Prediction with confidenceThreatLevel - Severity enum train(X, y, validation_split)
predict(features)
_calculate_metrics(y_true, y_pred)
_determine_threat_level(probability)
_get_decision_factors(features)
tune_threshold(X_val, y_val, target_fpr)
generate_synthetic_training_data()
save_model(filepath)
load_model(filepath)
defense_executor.py (550 lines) Purpose : Automated defense response execution
DefenseExecutor - Defense action orchestratorDefenseAction - Action trackingGasStrategy - Gas optimization execute_defense(strategy, target_contract, threat_tx_hash, threat_gas_price, urgency)
_calculate_gas_strategy(threat_gas_price, urgency)
_execute_pause_contract(action, contract, gas)
_wait_for_confirmation(action)
retry_failed_action(action)
get_statistics()
ids_engine.py (550 lines) Purpose : Main orchestration and management
IDSEngine - Main IDS controllerIDSConfig - Configuration managementThreatDetection - Detection event recordIDSStatistics - Operational metrics async def start ()
async def stop ()
_on_transaction_detected(tx)
async _analyze_transaction(tx)
_is_actionable_threat(classification)
async _handle_threat(tx, features, classification)
async _execute_defense(detection)
get_statistics()
export_detections(filepath)
Detection Algorithms
Feature Extraction (22 Dimensions)
Function Characteristics
Selector entropy (randomness indicator)
Unknown function flag
Parameter Patterns
Zero address presence
Max uint256 values
Suspiciously small/large values
Parameter count and complexity
Value & Gas Patterns
Log-scaled value
Gas price percentile
Gas price anomaly flag
Call Patterns
Estimated call depth
Delegatecall indicators
External call patterns
State change indicators
Exploit Signatures
Reentrancy pattern match
Flash loan pattern match
Price manipulation match
Behavioral Indicators
New sender flag
Timing anomalies
ML Classification Pipeline
features = analyzer.analyze(tx)
X_scaled = scaler.transform(features)
probabilities = model.predict_proba(X_scaled)
prob_malicious = probabilities[1 ]
is_malicious = prob_malicious >= threshold
if prob < 0.3 : level = BENIGN
elif prob < 0.5 : level = LOW
elif prob < 0.7 : level = MEDIUM
elif prob < 0.9 : level = HIGH
else : level = CRITICAL
Defense Execution Strategy
base_fee = latest_block.baseFeePerGas
threat_priority = threat_gas_price - base_fee
defense_priority = threat_priority * buffer_multiplier
max_fee = (base_fee * 1.2 ) + (defense_priority * 1.5 )
pause_tx = {
'from' : defender_address,
'to' : target_contract,
'data' : pause_function_selector,
'maxFeePerGas' : max_fee,
'maxPriorityFeePerGas' : defense_priority,
'gas' : gas_estimate * 1.2
}
signed_tx = defender_account.sign_transaction(pause_tx)
tx_hash = w3.eth.send_raw_transaction(signed_tx)
receipt = w3.eth.wait_for_transaction_receipt(tx_hash)
Usage Examples See README.md for comprehensive usage examples including:
Monitor-only mode (safe testing)
Active defense mode (production)
Individual component usage
Custom alert callbacks
Configuration tuning
Configuration
Threshold Tuning Classification Threshold (0.0-1.0):
0.5-0.6: High sensitivity, more false positives
0.7: Balanced (recommended for alerts)
0.8+: High precision (recommended for auto-defense)
LOW: Low confidence threats
MEDIUM: Moderate confidence
HIGH: Strong evidence (recommended for auto-defense)
CRITICAL: Confirmed exploits
Gas Buffer Multiplier
1.1-1.3: Cost-effective, lower success rate
1.5: Balanced (recommended)
2.0+: Maximum success rate, higher cost
Performance Metrics
Transaction detection: <100ms
Feature extraction: 50-100ms
ML classification: 10-20ms
Defense execution: 2-5 seconds
Total response time : 3-6 seconds
Can monitor 100+ contracts simultaneously
Processes 50-100 transactions/second
Scales horizontally with multiple instances
Accuracy (synthetic data):
Precision: 92-95%
Recall: 88-93%
F1 Score: 90-94%
False Positive Rate: <5%
Production Considerations
Security
Store private keys in secure key management (AWS KMS, Vault)
Use multi-sig wallets for high-value contracts
Implement rate limiting to prevent DoS
Regular security audits
Scalability
Deploy multiple IDS instances for redundancy
Use consensus mechanism for defense decisions
Load balance across multiple WebSocket endpoints
Cache extracted features
Monitoring
Track detection rate, false positive rate
Monitor defender account balance
Alert on connection failures
Log all defense actions to security system
Cost Management
Budget for gas costs (~0.01-0.1 ETH per defense)
Set up low-balance alerts
Consider gas price caps
Track ROI (saved vs spent)
Limitations
WebSocket Dependency : Requires stable WebSocket connectionGas Price Competition : Very sophisticated attackers may outbidNetwork Latency : Response time depends on network conditionsFalse Positives : ML models can misclassify legitimate transactionsTraining Data : Synthetic training data may not cover all exploit types
Future Enhancements
Advanced ML Models : Neural networks, LSTM for temporal patternsMulti-Chain Support : Polygon, BSC, ArbitrumCollaborative Detection : Share threat intelligence across instancesHistorical Analysis : Learn from past attacksSimulation Mode : Test defenses without real transactionsDAO Integration : Community-based defense decisions
References
Network IDS Principles : Snort, SuricataBlockchain Security : Trail of Bits, OpenZeppelinML for Security : Anomaly Detection in NetworksMEV Research : Flashbots, MEV-BoostGas Optimization : EIP-1559, Flashbots Protect
License MIT License - See LICENSE file for details
