# 詐欺パターンの特徴
FRAUD_PATTERNS = {
    'rug_pull': {
        'indicators': [
            'locked_liquidity_percentage < 50%',
            'developer_wallet_percentage > 10%',
            'contract_verification = False',
            'audit_status = None',
            'social_media_age < 30 days'
        ],
        'risk_level': 'CRITICAL'
    },
    'honeypot': {
        'indicators': [
            'sell_tax > 50%',
            'transfer_restrictions = True',
            'blacklist_function_exists = True',
            'pausable_contract = True'
        ],
        'risk_level': 'HIGH'
    },
    'wash_trading': {
        'indicators': [
            'circular_trading_pattern = True',
            'volume_concentration > 80%',
            'unique_traders < 100',
            'price_volatility < 1%'
        ],
        'risk_level': 'MEDIUM'
    }
}

from web3 import Web3
import json
from eth_abi import decode_abi

class SmartContractAnalyzer:
    def __init__(self, web3_provider):
        self.w3 = Web3(Web3.HTTPProvider(web3_provider))
        self.known_patterns = self.load_malicious_patterns()

    def analyze_contract(self, contract_address):
        """スマートコントラクトの詳細分析"""
        analysis_result = {
            'address': contract_address,
            'risks': [],
            'score': 0,
            'details': {}
        }

        # コントラクトコードの取得
        bytecode = self.w3.eth.get_code(contract_address)

        if bytecode == b'':
            analysis_result['risks'].append('NOT_A_CONTRACT')
            return analysis_result

        # 検証状態のチェック
        is_verified = self.check_contract_verification(contract_address)
        if not is_verified:
            analysis_result['risks'].append('UNVERIFIED_CONTRACT')
            analysis_result['score'] += 20

        # 危険な関数の検出
        dangerous_functions = self.detect_dangerous_functions(bytecode)
        if dangerous_functions:
            analysis_result['risks'].extend(dangerous_functions)
            analysis_result['score'] += len(dangerous_functions) * 15

        # 所有権の分析
        ownership_risks = self.analyze_ownership(contract_address)
        analysis_result['risks'].extend(ownership_risks)
        analysis_result['score'] += len(ownership_risks) * 10

        # トークノミクスの分析
        if self.is_token_contract(contract_address):
            token_risks = self.analyze_tokenomics(contract_address)
            analysis_result['risks'].extend(token_risks)
            analysis_result['score'] += sum(risk['severity'] for risk in token_risks)

        return analysis_result

    def detect_dangerous_functions(self, bytecode):
        """危険な関数パターンの検出"""
        dangerous_patterns = {
            'SELFDESTRUCT': b'\xff',  # 自己破壊
            'DELEGATECALL': b'\xf4',  # 委任呼び出し
            'CREATE2': b'\xf5',       # 予測可能なアドレス生成
        }

        found_risks = []
        bytecode_hex = bytecode.hex()

        for name, pattern in dangerous_patterns.items():
            if pattern.hex() in bytecode_hex:
                found_risks.append(f'DANGEROUS_OPCODE_{name}')

        # 隠れたミント機能の検出
        mint_patterns = [
            'mint', '_mint', 'issue', '_issue'
        ]
        for pattern in mint_patterns:
            if pattern.encode().hex() in bytecode_hex:
                found_risks.append('HIDDEN_MINT_FUNCTION')
                break

        return found_risks

    def analyze_ownership(self, contract_address):
        """所有権とアクセス制御の分析"""
        risks = []

        try:
            # Ownable パターンのチェック
            owner_slot = self.w3.eth.get_storage_at(contract_address, 0)
            if owner_slot != b'\x00' * 32:
                owner_address = Web3.toChecksumAddress(owner_slot[-20:])

                # 所有者のトランザクション履歴分析
                owner_tx_count = self.w3.eth.get_transaction_count(owner_address)

                if owner_tx_count < 10:
                    risks.append('NEW_OWNER_ADDRESS')

                # マルチシグかどうかチェック
                owner_code = self.w3.eth.get_code(owner_address)
                if owner_code == b'':
                    risks.append('EOA_OWNER')  # 個人アドレスが所有者

        except Exception as e:
            risks.append('OWNERSHIP_CHECK_FAILED')

        return risks

    def analyze_tokenomics(self, contract_address):
        """トークノミクスの分析"""
        risks = []

        try:
            # 基本的なERC20関数の呼び出し
            contract = self.get_token_contract(contract_address)

            total_supply = contract.functions.totalSupply().call()

            # 大口保有者の分析
            holder_analysis = self.analyze_token_holders(contract_address, total_supply)

            if holder_analysis['concentration'] > 0.5:
                risks.append({
                    'type': 'HIGH_CONCENTRATION',
                    'severity': 30,
                    'details': f"Top holders own {holder_analysis['concentration']*100:.1f}%"
                })

            # 流動性プールの分析
            liquidity_analysis = self.analyze_liquidity_pools(contract_address)

            if liquidity_analysis['locked_percentage'] < 0.5:
                risks.append({
                    'type': 'LOW_LOCKED_LIQUIDITY',
                    'severity': 25,
                    'details': f"Only {liquidity_analysis['locked_percentage']*100:.1f}% locked"
                })

        except Exception as e:
            risks.append({
                'type': 'TOKENOMICS_ANALYSIS_FAILED',
                'severity': 10,
                'details': str(e)
            })

        return risks

import networkx as nx
from collections import defaultdict
import numpy as np

class TransactionPatternAnalyzer:
    def __init__(self):
        self.graph = nx.DiGraph()
        self.address_features = defaultdict(dict)

    def analyze_transaction_flow(self, transactions):
        """トランザクションフローの分析"""
        # グラフの構築
        for tx in transactions:
            self.graph.add_edge(
                tx['from'],
                tx['to'],
                value=float(tx['value']),
                timestamp=tx['timestamp'],
                hash=tx['hash']
            )

        # 循環取引の検出
        cycles = self.detect_circular_trading()

        # ミキシングパターンの検出
        mixing_patterns = self.detect_mixing_patterns()

        # 異常な資金フローの検出
        anomalous_flows = self.detect_anomalous_flows()

        return {
            'circular_trading': cycles,
            'mixing_patterns': mixing_patterns,
            'anomalous_flows': anomalous_flows,
            'risk_addresses': self.identify_risk_addresses()
        }

    def detect_circular_trading(self):
        """循環取引（ウォッシュトレーディング）の検出"""
        cycles = list(nx.simple_cycles(self.graph))

        suspicious_cycles = []
        for cycle in cycles:
            if len(cycle) <= 5:  # 5アドレス以内の循環
                cycle_volume = self.calculate_cycle_volume(cycle)
                if cycle_volume > 10000:  # 閾値以上の取引量
                    suspicious_cycles.append({
                        'addresses': cycle,
                        'volume': cycle_volume,
                        'pattern': 'WASH_TRADING'
                    })

        return suspicious_cycles

    def detect_mixing_patterns(self):
        """ミキシングサービスのパターン検出"""
        mixing_indicators = []

        for node in self.graph.nodes():
            in_degree = self.graph.in_degree(node)
            out_degree = self.graph.out_degree(node)

            # 多数の入出力を持つアドレス
            if in_degree > 50 and out_degree > 50:
                # 金額の分散を確認
                in_amounts = [self.graph[u][node]['value'] for u in self.graph.predecessors(node)]
                out_amounts = [self.graph[node][v]['value'] for v in self.graph.successors(node)]

                # 金額が類似している場合
                if np.std(in_amounts) / np.mean(in_amounts) < 0.1:
                    mixing_indicators.append({
                        'address': node,
                        'pattern': 'MIXER_PATTERN',
                        'confidence': 0.8
                    })

        return mixing_indicators

    def detect_anomalous_flows(self):
        """異常な資金フローの検出"""
        anomalies = []

        # PageRankを使った中心性分析
        pagerank = nx.pagerank(self.graph, weight='value')

        # 異常に高い中心性を持つアドレス
        mean_pr = np.mean(list(pagerank.values()))
        std_pr = np.std(list(pagerank.values()))

        for address, pr_score in pagerank.items():
            if pr_score > mean_pr + 3 * std_pr:
                # 詳細な分析
                flow_analysis = self.analyze_address_flows(address)

                if flow_analysis['is_suspicious']:
                    anomalies.append({
                        'address': address,
                        'pagerank_score': pr_score,
                        'flow_pattern': flow_analysis['pattern'],
                        'risk_level': flow_analysis['risk_level']
                    })

        return anomalies

class MEVDetector:
    def __init__(self):
        self.known_mev_bots = self.load_known_bots()
        self.sandwich_patterns = []

    def detect_mev_activity(self, block_transactions):
        """MEVアクティビティの検出"""
        mev_incidents = {
            'frontrunning': [],
            'sandwich_attacks': [],
            'arbitrage': [],
            'liquidations': []
        }

        # トランザクションをガス価格でソート
        sorted_txs = sorted(block_transactions, key=lambda x: x['gasPrice'], reverse=True)

        for i, tx in enumerate(sorted_txs):
            # フロントランニングの検出
            if self.is_frontrunning(tx, sorted_txs[i+1:]):
                mev_incidents['frontrunning'].append({
                    'transaction': tx['hash'],
                    'victim_tx': self.find_victim_tx(tx, sorted_txs[i+1:]),
                    'profit_estimate': self.estimate_profit(tx)
                })

            # サンドイッチ攻撃の検出
            sandwich = self.detect_sandwich_attack(tx, block_transactions)
            if sandwich:
                mev_incidents['sandwich_attacks'].append(sandwich)

        return mev_incidents

    def detect_sandwich_attack(self, tx, all_txs):
        """サンドイッチ攻撃の検出"""
        # 同じDEXルーターへの連続した取引を探す
        if 'to' not in tx or not self.is_dex_router(tx['to']):
            return None

        tx_index = next(i for i, t in enumerate(all_txs) if t['hash'] == tx['hash'])

        # 前後のトランザクションをチェック
        for i in range(max(0, tx_index - 2), min(len(all_txs), tx_index + 3)):
            if i == tx_index:
                continue

            other_tx = all_txs[i]

            # 同じトークンペアでの取引かチェック
            if self.same_token_pair(tx, other_tx):
                # タイミングと価格影響を分析
                if self.is_sandwich_pattern(tx, other_tx, all_txs):
                    return {
                        'attacker_tx': [tx['hash'], other_tx['hash']],
                        'victim_tx': self.find_sandwiched_tx(tx, other_tx, all_txs),
                        'estimated_profit': self.calculate_sandwich_profit(tx, other_tx)
                    }

        return None

class FraudFeatureExtractor:
    def __init__(self):
        self.feature_names = []

    def extract_features(self, address_data, transaction_data, contract_data=None):
        """包括的な特徴量抽出"""
        features = {}

        # アドレスレベルの特徴
        address_features = self.extract_address_features(address_data)
        features.update(address_features)

        # トランザクションパターンの特徴
        tx_features = self.extract_transaction_features(transaction_data)
        features.update(tx_features)

        # コントラクト特徴（該当する場合）
        if contract_data:
            contract_features = self.extract_contract_features(contract_data)
            features.update(contract_features)

        # 時系列特徴
        temporal_features = self.extract_temporal_features(transaction_data)
        features.update(temporal_features)

        # ネットワーク特徴
        network_features = self.extract_network_features(address_data, transaction_data)
        features.update(network_features)

        return features

    def extract_address_features(self, address_data):
        """アドレスレベルの特徴量"""
        return {
            # 基本統計
            'total_transactions': address_data['tx_count'],
            'unique_counterparties': address_data['unique_addresses'],
            'account_age_days': address_data['age_days'],
            'total_volume': address_data['total_volume'],

            # 行動パターン
            'avg_tx_value': address_data['total_volume'] / max(address_data['tx_count'], 1),
            'tx_frequency': address_data['tx_count'] / max(address_data['age_days'], 1),
            'dormancy_ratio': address_data['inactive_days'] / max(address_data['age_days'], 1),

            # リスク指標
            'known_exchange_interaction': address_data.get('exchange_interactions', 0),
            'mixer_interaction': address_data.get('mixer_interactions', 0),
            'contract_creation_count': address_data.get('contracts_created', 0)
        }

    def extract_transaction_features(self, transaction_data):
        """トランザクションパターンの特徴量"""
        tx_values = [tx['value'] for tx in transaction_data]
        tx_times = [tx['timestamp'] for tx in transaction_data]

        # 時間間隔
        time_intervals = np.diff(sorted(tx_times))

        return {
            # 値の統計
            'tx_value_mean': np.mean(tx_values),
            'tx_value_std': np.std(tx_values),
            'tx_value_skew': self.calculate_skewness(tx_values),
            'tx_value_kurtosis': self.calculate_kurtosis(tx_values),

            # 時間パターン
            'avg_time_between_tx': np.mean(time_intervals) if len(time_intervals) > 0 else 0,
            'time_interval_std': np.std(time_intervals) if len(time_intervals) > 0 else 0,
            'burst_transaction_ratio': self.calculate_burst_ratio(tx_times),

            # ガス使用パターン
            'avg_gas_price': np.mean([tx['gasPrice'] for tx in transaction_data]),
            'gas_price_variance': np.var([tx['gasPrice'] for tx in transaction_data]),
            'failed_tx_ratio': sum(1 for tx in transaction_data if tx['status'] == 0) / len(transaction_data)
        }

    def extract_network_features(self, address_data, transaction_data):
        """ネットワークグラフベースの特徴量"""
        # グラフ構築
        G = nx.Graph()
        for tx in transaction_data:
            G.add_edge(tx['from'], tx['to'], weight=tx['value'])

        address = address_data['address']

        # 中心性指標
        features = {}
        if address in G:
            features['degree_centrality'] = nx.degree_centrality(G)[address]
            features['betweenness_centrality'] = nx.betweenness_centrality(G).get(address, 0)
            features['clustering_coefficient'] = nx.clustering(G, address)

            # k-hop近傍の分析
            k_hop_neighbors = self.get_k_hop_neighbors(G, address, k=2)
            features['two_hop_neighbors'] = len(k_hop_neighbors)

        return features

from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.neural_network import MLPClassifier
import xgboost as xgb

class EnsembleFraudDetector:
    def __init__(self):
        self.models = {
            'random_forest': RandomForestClassifier(
                n_estimators=200,
                max_depth=15,
                min_samples_split=5,
                class_weight='balanced'
            ),
            'gradient_boosting': GradientBoostingClassifier(
                n_estimators=150,
                learning_rate=0.1,
                max_depth=10
            ),
            'xgboost': xgb.XGBClassifier(
                n_estimators=200,
                learning_rate=0.1,
                max_depth=10,
                scale_pos_weight=10  # 不均衡データ対策
            ),
            'neural_network': MLPClassifier(
                hidden_layer_sizes=(100, 50, 25),
                activation='relu',
                solver='adam',
                early_stopping=True
            )
        }

        self.model_weights = {
            'random_forest': 0.3,
            'gradient_boosting': 0.25,
            'xgboost': 0.3,
            'neural_network': 0.15
        }

        self.feature_extractor = FraudFeatureExtractor()
        self.is_trained = False

    def train(self, training_data, labels):
        """アンサンブルモデルの訓練"""
        # 特徴量抽出
        X = self.extract_all_features(training_data)
        y = labels

        # 各モデルの訓練
        for name, model in self.models.items():
            print(f"Training {name}...")
            model.fit(X, y)

        self.is_trained = True

        # 特徴量重要度の分析
        self.analyze_feature_importance(X)

    def predict_fraud_probability(self, data):
        """詐欺確率の予測"""
        if not self.is_trained:
            raise ValueError("Model must be trained first")

        # 特徴量抽出
        features = self.extract_features_single(data)
        X = np.array([features])

        # 各モデルの予測
        predictions = {}
        for name, model in self.models.items():
            prob = model.predict_proba(X)[0][1]  # 詐欺クラスの確率
            predictions[name] = prob

        # 加重平均
        final_probability = sum(
            predictions[name] * self.model_weights[name]
            for name in predictions
        )

        # 詳細な分析結果
        return {
            'fraud_probability': final_probability,
            'risk_level': self.get_risk_level(final_probability),
            'model_predictions': predictions,
            'contributing_factors': self.explain_prediction(features, predictions)
        }

    def explain_prediction(self, features, predictions):
        """予測の説明（特徴量の寄与度）"""
        # SHAP値やLIMEを使用した説明可能性の実装
        explanations = []

        # Random Forestの特徴量重要度を使用
        rf_model = self.models['random_forest']
        feature_importance = rf_model.feature_importances_

        # 重要な特徴量トップ5
        important_features = sorted(
            zip(self.feature_names, feature_importance),
            key=lambda x: x[1],
            reverse=True
        )[:5]

        for feature_name, importance in important_features:
            feature_value = features[self.feature_names.index(feature_name)]
            explanations.append({
                'feature': feature_name,
                'value': feature_value,
                'importance': importance,
                'interpretation': self.interpret_feature(feature_name, feature_value)
            })

        return explanations

class RealTimeFraudDetectionSystem:
    def __init__(self):
        self.fraud_detector = EnsembleFraudDetector()
        self.contract_analyzer = SmartContractAnalyzer()
        self.transaction_analyzer = TransactionPatternAnalyzer()
        self.mev_detector = MEVDetector()
        self.alert_system = FraudAlertSystem()
        self.blacklist = self.load_blacklist()

    async def monitor_address(self, address):
        """アドレスのリアルタイム監視"""
        while True:
            try:
                # 最新のデータ取得
                current_data = await self.fetch_latest_data(address)

                # 複数の分析を並行実行
                analysis_results = await asyncio.gather(
                    self.analyze_transactions(current_data['transactions']),
                    self.analyze_contract_risk(address),
                    self.detect_mev_activity(current_data['recent_blocks']),
                    self.check_blacklist(address)
                )

                # 総合的なリスク評価
                risk_assessment = self.assess_overall_risk(analysis_results)

                # アラート判定
                if risk_assessment['risk_score'] > 0.7:
                    await self.alert_system.send_high_risk_alert(
                        address,
                        risk_assessment
                    )

                # 継続的な学習
                self.update_models(current_data, risk_assessment)

            except Exception as e:
                print(f"Error monitoring {address}: {e}")

            await asyncio.sleep(60)  # 1分ごとに更新

    async def analyze_transactions(self, transactions):
        """トランザクションの分析"""
        # パターン分析
        patterns = self.transaction_analyzer.analyze_transaction_flow(transactions)

        # 機械学習による詐欺検知
        fraud_probability = self.fraud_detector.predict_fraud_probability({
            'transactions': transactions,
            'patterns': patterns
        })

        return {
            'fraud_probability': fraud_probability,
            'suspicious_patterns': patterns,
            'risk_transactions': self.identify_risk_transactions(transactions)
        }

    def assess_overall_risk(self, analysis_results):
        """総合的なリスク評価"""
        # 各分析結果の重み付け集約
        weights = {
            'transaction_analysis': 0.3,
            'contract_risk': 0.25,
            'mev_activity': 0.2,
            'blacklist_check': 0.25
        }

        total_risk = 0
        risk_factors = []

        for i, (analysis_type, weight) in enumerate(weights.items()):
            if analysis_results[i]:
                risk_score = self.extract_risk_score(analysis_results[i])
                total_risk += risk_score * weight

                if risk_score > 0.5:
                    risk_factors.append({
                        'type': analysis_type,
                        'score': risk_score,
                        'details': analysis_results[i]
                    })

        return {
            'risk_score': total_risk,
            'risk_level': self.get_risk_level(total_risk),
            'risk_factors': risk_factors,
            'recommended_actions': self.get_recommended_actions(risk_factors)
        }

import torch
import torch.nn as nn
from torch_geometric.nn import GCNConv, global_mean_pool

class FraudGNN(nn.Module):
    def __init__(self, num_features, hidden_dim=64):
        super(FraudGNN, self).__init__()
        self.conv1 = GCNConv(num_features, hidden_dim)
        self.conv2 = GCNConv(hidden_dim, hidden_dim)
        self.conv3 = GCNConv(hidden_dim, 32)

        self.fc1 = nn.Linear(32, 16)
        self.fc2 = nn.Linear(16, 2)  # 2クラス分類（正常/詐欺）

        self.dropout = nn.Dropout(0.3)
        self.relu = nn.ReLU()

    def forward(self, x, edge_index, batch):
        # グラフ畳み込み層
        x = self.relu(self.conv1(x, edge_index))
        x = self.dropout(x)

        x = self.relu(self.conv2(x, edge_index))
        x = self.dropout(x)

        x = self.relu(self.conv3(x, edge_index))

        # グローバルプーリング
        x = global_mean_pool(x, batch)

        # 全結合層
        x = self.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)

        return torch.log_softmax(x, dim=1)

class GraphBasedFraudDetector:
    def __init__(self):
        self.model = FraudGNN(num_features=20)
        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=0.001)
        self.criterion = nn.NLLLoss()

    def prepare_graph_data(self, transactions):
        """トランザクションデータからグラフ構造を作成"""
        # ノード特徴量の抽出
        node_features = self.extract_node_features(transactions)

        # エッジリストの作成
        edge_list = []
        for tx in transactions:
            from_idx = self.get_node_index(tx['from'])
            to_idx = self.get_node_index(tx['to'])
            edge_list.append([from_idx, to_idx])

        edge_index = torch.tensor(edge_list, dtype=torch.long).t().contiguous()

        return {
            'x': torch.tensor(node_features, dtype=torch.float),
            'edge_index': edge_index
        }

    def train_epoch(self, train_loader):
        """1エポックの訓練"""
        self.model.train()
        total_loss = 0

        for batch in train_loader:
            self.optimizer.zero_grad()

            # 順伝播
            out = self.model(batch.x, batch.edge_index, batch.batch)
            loss = self.criterion(out, batch.y)

            # 逆伝播
            loss.backward()
            self.optimizer.step()

            total_loss += loss.item()

        return total_loss / len(train_loader)

class FraudTransformer(nn.Module):
    def __init__(self, input_dim, d_model=128, nhead=8, num_layers=6):
        super(FraudTransformer, self).__init__()

        self.input_projection = nn.Linear(input_dim, d_model)
        self.positional_encoding = PositionalEncoding(d_model)

        encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model,
            nhead=nhead,
            dim_feedforward=512,
            dropout=0.1
        )

        self.transformer_encoder = nn.TransformerEncoder(
            encoder_layer,
            num_layers=num_layers
        )

        self.fraud_classifier = nn.Sequential(
            nn.Linear(d_model, 64),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(64, 2)
        )

    def forward(self, x):
        # 入力投影と位置エンコーディング
        x = self.input_projection(x)
        x = self.positional_encoding(x)

        # Transformer エンコーディング
        x = self.transformer_encoder(x)

        # 集約と分類
        x = x.mean(dim=1)  # 時系列の平均
        output = self.fraud_classifier(x)

        return output

class PrivacyPreservingDetector:
    def __init__(self):
        self.differential_privacy = DifferentialPrivacy(epsilon=1.0)
        self.federated_learning = FederatedLearning()

    def anonymize_addresses(self, addresses):
        """アドレスの匿名化"""
        # ハッシュ化による匿名化
        anonymized = {}
        for addr in addresses:
            anonymized[addr] = hashlib.sha256(
                addr.encode() + self.salt
            ).hexdigest()[:16]

        return anonymized

    def apply_differential_privacy(self, features):
        """差分プライバシーの適用"""
        # ラプラスノイズの追加
        sensitivity = self.calculate_sensitivity(features)
        noise = np.random.laplace(0, sensitivity / self.epsilon, features.shape)

        return features + noise

class ModelMonitoringSystem:
    def __init__(self):
        self.performance_metrics = defaultdict(list)
        self.drift_detector = DriftDetector()
        self.retraining_scheduler = RetrainingScheduler()

    def monitor_model_performance(self, predictions, actuals):
        """モデルパフォーマンスの監視"""
        # 精度指標の計算
        metrics = {
            'accuracy': accuracy_score(actuals, predictions),
            'precision': precision_score(actuals, predictions),
            'recall': recall_score(actuals, predictions),
            'f1': f1_score(actuals, predictions)
        }

        # 履歴に追加
        for metric, value in metrics.items():
            self.performance_metrics[metric].append({
                'timestamp': datetime.now(),
                'value': value
            })

        # ドリフト検出
        if self.drift_detector.detect_drift(self.performance_metrics):
            self.trigger_retraining()

        return metrics

    def trigger_retraining(self):
        """再訓練のトリガー"""
        print("Model drift detected! Scheduling retraining...")
        self.retraining_scheduler.schedule_retraining()

class CompleteFraudDetectionPipeline:
    def __init__(self):
        self.data_collector = BlockchainDataCollector()
        self.feature_extractor = FraudFeatureExtractor()
        self.ensemble_detector = EnsembleFraudDetector()
        self.gnn_detector = GraphBasedFraudDetector()
        self.alert_manager = AlertManager()
        self.database = FraudDatabase()

    async def run_pipeline(self):
        """詐欺検知パイプラインの実行"""
        while True:
            try:
                # 1. データ収集
                new_data = await self.data_collector.collect_latest_data()

                # 2. 前処理と特徴量抽出
                processed_data = self.preprocess_data(new_data)
                features = self.feature_extractor.extract_features(processed_data)

                # 3. 複数モデルでの検知
                detection_results = await self.run_detections(features, processed_data)

                # 4. 結果の統合と評価
                final_assessment = self.integrate_results(detection_results)

                # 5. アクション実行
                if final_assessment['is_fraud']:
                    await self.execute_fraud_response(final_assessment)

                # 6. データベースへの記録
                await self.database.store_detection_result(final_assessment)

                # 7. モデルの更新（必要に応じて）
                self.update_models_if_needed(final_assessment)

            except Exception as e:
                print(f"Pipeline error: {e}")
                await self.handle_error(e)

            await asyncio.sleep(30)  # 30秒ごとに実行

    async def execute_fraud_response(self, assessment):
        """詐欺検知時の対応"""
        severity = assessment['severity']

        if severity == 'CRITICAL':
            # 即座にアラート送信
            await self.alert_manager.send_critical_alert(assessment)

            # 関連アドレスの自動ブロック
            await self.block_related_addresses(assessment['address'])

            # 取引所への通知
            await self.notify_exchanges(assessment)

        elif severity == 'HIGH':
            # 警告アラート
            await self.alert_manager.send_warning_alert(assessment)

            # 監視リストへの追加
            await self.add_to_watchlist(assessment['address'])