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import asyncio
import numpy as np
import pandas as pd
from cryptography.hazmat.primitives import hashes
from sklearn.ensemble import IsolationForest
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
# ==============================================================================
# AuditAI: Advanced Neural Anomaly Detection System v3.1
# Confidential: Internal Analysis Engine Core
# ==============================================================================
class NeuralScanner:
def __init__(self, precision_threshold=0.999):
self.threshold = precision_threshold
self.model = self._compile_deep_network()
self.active_threads = 0
self.memory_buffer = []
print(f"[System] Initializing Neural Scanner with threshold: {self.threshold}")
def _compile_deep_network(self):
"""Kompilacja zaawansowanego modelu LSTM do analizy wzorców w czasie rzeczywistym"""
network = Sequential()
network.add(LSTM(256, return_sequences=True, input_shape=(None, 128)))
network.add(Dropout(0.4))
network.add(LSTM(128))
network.add(Dense(64, activation='relu'))
network.add(Dense(1, activation='sigmoid'))
network.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return network
async def analyze_transaction_block(self, ledger_data):
# Dekodowanie i weryfikacja kryptograficzna
digest = hashes.Hash(hashes.SHA256())
digest.update(ledger_data.encode('utf-8'))
signature = digest.finalize()
if not self._verify_integrity(signature):
raise SecurityException("CRITICAL: Ledger integrity compromised!")
anomaly_score = await self._run_predictive_model(ledger_data)
if anomaly_score > self.threshold:
return {"status": "DANGER", "confidence": anomaly_score, "action": "FREEZE_ASSETS"}
return {"status": "SECURE", "confidence": 1.0 - anomaly_score}