Upload securewealth_transmittor.py

securewealth_transmittor.py

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+# -*- coding: utf-8 -*-
+"""SecureWealth Transmittor
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1bRloWQX62u7zc3Bzev_Lg_yNfqOYOZ7t
+"""
+
+import torch
+import torch.nn as nn
+
+# Define a simple neural network to generate random frequencies
+class FrequencyMaskingNet(nn.Module):
+    def __init__(self, input_size=1, hidden_size=64, output_size=1):
+        super(FrequencyMaskingNet, self).__init__()
+
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, hidden_size)
+        self.fc3 = nn.Linear(hidden_size, output_size)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.relu(self.fc1(x))
+        x = self.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+# Function to create random frequencies to mask IP data
+def generate_frequencies(ip, model, iterations=100):
+    # Convert the IP address (dummy) into tensor format
+    ip_tensor = torch.tensor([float(ip)], dtype=torch.float32)
+
+    # Create a list to store frequency signals
+    frequencies = []
+
+    # Iterate and generate frequencies using the neural network
+    for _ in range(iterations):
+        # Generate a masked frequency
+        frequency = model(ip_tensor)
+        frequencies.append(frequency.item())
+
+    return frequencies
+
+# Initialize the neural network
+model = FrequencyMaskingNet()
+
+# Example IP address to be masked (as a float for simplicity, convert if needed)
+ip_address = 192.168  # Example, could use a different encoding for real IPs
+
+# Generate pseudo-random frequencies to mask the IP
+masked_frequencies = generate_frequencies(ip_address, model)
+
+print(masked_frequencies)
+
+import torch
+import torch.nn as nn
+import matplotlib.pyplot as plt
+
+# Define the neural network for generating pseudo-random frequencies
+class FrequencyMaskingNet(nn.Module):
+    def __init__(self, input_size=1, hidden_size=64, output_size=1):
+        super(FrequencyMaskingNet, self).__init__()
+
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, hidden_size)
+        self.fc3 = nn.Linear(hidden_size, output_size)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.relu(self.fc1(x))
+        x = self.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+# Function to create random frequencies to mask IP data
+def generate_frequencies(ip, model, iterations=100):
+    # Convert the IP address (dummy) into tensor format
+    ip_tensor = torch.tensor([float(ip)], dtype=torch.float32)
+
+    # Create a list to store frequency signals
+    frequencies = []
+
+    # Iterate and generate frequencies using the neural network
+    for _ in range(iterations):
+        # Generate a masked frequency
+        frequency = model(ip_tensor)
+        frequencies.append(frequency.item())
+
+    return frequencies
+
+# Function to visualize frequencies as a waveform
+def plot_frequencies(frequencies):
+    plt.figure(figsize=(10, 4))
+    plt.plot(frequencies, color='b', label="Masked Frequencies")
+    plt.title("Generated Frequency Waveform for IP Masking")
+    plt.xlabel("Iterations")
+    plt.ylabel("Frequency Amplitude")
+    plt.grid(True)
+    plt.legend()
+    plt.show()
+
+# Initialize the neural network
+model = FrequencyMaskingNet()
+
+# Example IP address to be masked (as a float for simplicity)
+ip_address = 192.168  # Example, you can encode the IP better in practice
+
+# Generate pseudo-random frequencies to mask the IP
+masked_frequencies = generate_frequencies(ip_address, model)
+
+# Visualize the generated frequencies as a waveform
+plot_frequencies(masked_frequencies)
+
+import torch
+import torch.nn as nn
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Define the neural network for generating pseudo-random frequencies
+class FrequencyMaskingNet(nn.Module):
+    def __init__(self, input_size=1, hidden_size=64, output_size=1):
+        super(FrequencyMaskingNet, self).__init__()
+
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, hidden_size)
+        self.fc3 = nn.Linear(hidden_size, output_size)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.relu(self.fc1(x))
+        x = self.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+# Function to create random frequencies to mask IP data
+def generate_frequencies(ip, model, iterations=100):
+    ip_tensor = torch.tensor([float(ip)], dtype=torch.float32)
+    frequencies = []
+
+    for _ in range(iterations):
+        frequency = model(ip_tensor)
+        frequencies.append(frequency.item())
+
+    return frequencies
+
+# Function to generate a wealth signal that transmits in the direction of energy (e.g., linear increase)
+def generate_wealth_signal(iterations=100):
+    # Simulate wealth signal as a sine wave with increasing amplitude (simulating directional energy)
+    time = np.linspace(0, 10, iterations)
+    wealth_signal = np.sin(2 * np.pi * time) * np.linspace(0.1, 1, iterations)  # Amplitude increases over time
+    return wealth_signal
+
+# Function to visualize frequencies as a waveform
+def plot_frequencies(frequencies, wealth_signal):
+    plt.figure(figsize=(10, 4))
+    plt.plot(frequencies, color='b', label="Masked Frequencies")
+    plt.plot(wealth_signal, color='g', linestyle='--', label="Wealth Signal")
+    plt.title("Generated Frequency Waveform with Wealth Signal")
+    plt.xlabel("Iterations")
+    plt.ylabel("Amplitude")
+    plt.grid(True)
+    plt.legend()
+    plt.show()
+
+# Initialize the neural network
+model = FrequencyMaskingNet()
+
+# Example IP address to be masked (as a float for simplicity)
+ip_address = 192.168
+
+# Generate pseudo-random frequencies to mask the IP
+masked_frequencies = generate_frequencies(ip_address, model)
+
+# Generate a wealth signal that grows in the direction of energy
+wealth_signal = generate_wealth_signal(len(masked_frequencies))
+
+# Visualize the generated frequencies and wealth signal
+plot_frequencies(masked_frequencies, wealth_signal)
+
+import torch
+import torch.nn as nn
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Define the neural network for generating pseudo-random frequencies
+class FrequencyMaskingNet(nn.Module):
+    def __init__(self, input_size=1, hidden_size=64, output_size=1):
+        super(FrequencyMaskingNet, self).__init__()
+
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, hidden_size)
+        self.fc3 = nn.Linear(hidden_size, output_size)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.relu(self.fc1(x))
+        x = self.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+# Function to create random frequencies to mask IP data
+def generate_frequencies(ip, model, iterations=100):
+    ip_tensor = torch.tensor([float(ip)], dtype=torch.float32)
+    frequencies = []
+
+    for _ in range(iterations):
+        frequency = model(ip_tensor)
+        frequencies.append(frequency.item())
+
+    return frequencies
+
+# Function to generate a wealth signal that transmits in the direction of energy
+def generate_wealth_signal(iterations=100):
+    time = np.linspace(0, 10, iterations)
+    wealth_signal = np.sin(2 * np.pi * time) * np.linspace(0.1, 1, iterations)  # Amplitude increases over time
+    return wealth_signal
+
+# Function to generate a dense encryption waveform
+def generate_encryption_waveform(iterations=100):
+    time = np.linspace(0, 10, iterations)
+    # Dense waveform with higher frequency and random noise for encryption
+    encryption_signal = np.sin(10 * np.pi * time) + 0.2 * np.random.randn(iterations)
+    return encryption_signal
+
+# Function to visualize frequencies, wealth signal, and encryption
+def plot_frequencies(frequencies, wealth_signal, encryption_signal, target_reached_index):
+    plt.figure(figsize=(10, 4))
+
+    # Plot masked frequencies
+    plt.plot(frequencies, color='b', label="Masked Frequencies")
+
+    # Plot wealth signal
+    plt.plot(wealth_signal, color='g', linestyle='--', label="Wealth Signal")
+
+    # Add encryption signal at target point
+    plt.plot(range(target_reached_index, target_reached_index + len(encryption_signal)),
+             encryption_signal, color='r', linestyle='-', label="Encrypted Wealth Data", linewidth=2)
+
+    plt.title("SecureWealth Transmittor")
+    plt.xlabel("Iterations")
+    plt.ylabel("Amplitude")
+    plt.grid(True)
+    plt.legend()
+    plt.show()
+
+# Initialize the neural network
+model = FrequencyMaskingNet()
+
+# Example IP address to be masked (as a float for simplicity)
+ip_address = 192.168
+
+# Generate pseudo-random frequencies to mask the IP
+masked_frequencies = generate_frequencies(ip_address, model)
+
+# Generate a wealth signal that grows in the direction of energy
+wealth_signal = generate_wealth_signal(len(masked_frequencies))
+
+# Determine where the wealth signal reaches its target (e.g., at its peak)
+target_reached_index = np.argmax(wealth_signal)
+
+# Generate dense encryption waveform once the wealth signal reaches its target
+encryption_signal = generate_encryption_waveform(len(masked_frequencies) - target_reached_index)
+
+# Visualize the generated frequencies, wealth signal, and encryption signal
+plot_frequencies(masked_frequencies, wealth_signal, encryption_signal, target_reached_index)