Upload secure_pulse.py

secure_pulse.py

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+# -*- coding: utf-8 -*-
+"""Secure Pulse
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1tY1tdoA1k0yP7_rFc5ckKLIYm-50jvYJ
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class FrequencyModulation(nn.Module):
+    def __init__(self, frequency):
+        super(FrequencyModulation, self).__init__()
+        self.frequency = frequency
+
+    def forward(self, x):
+        # Apply a sine function for modulation
+        return torch.sin(2 * torch.pi * self.frequency * x)
+
+class EncryptionLayer(nn.Module):
+    def __init__(self, key_frequency):
+        super(EncryptionLayer, self).__init__()
+        self.key_frequency = key_frequency
+
+    def forward(self, x):
+        # Apply frequency shift as a form of encryption
+        return torch.sin(2 * torch.pi * (self.key_frequency + x))
+
+class FrequencyHopping(nn.Module):
+    def __init__(self, frequencies):
+        super(FrequencyHopping, self).__init__()
+        self.frequencies = frequencies
+
+    def forward(self, x):
+        # Apply frequency hopping
+        for freq in self.frequencies:
+            x = torch.sin(2 * torch.pi * freq * x)
+        return x
+
+class DecryptionLayer(nn.Module):
+    def __init__(self, key_frequency):
+        super(DecryptionLayer, self).__init__()
+        self.key_frequency = key_frequency
+
+    def forward(self, x):
+        # Inverse of the encryption process
+        return torch.asin(x) / (2 * torch.pi * self.key_frequency)
+
+class FrequencyVPN(nn.Module):
+    def __init__(self, frequency, key_frequency, hopping_frequencies):
+        super(FrequencyVPN, self).__init__()
+        self.modulation = FrequencyModulation(frequency)
+        self.encryption = EncryptionLayer(key_frequency)
+        self.hopping = FrequencyHopping(hopping_frequencies)
+        self.decryption = DecryptionLayer(key_frequency)
+
+    def forward(self, x):
+        x = self.modulation(x)
+        x = self.encryption(x)
+        x = self.hopping(x)
+        return self.decryption(x)
+
+# Example usage
+model = FrequencyVPN(frequency=5, key_frequency=10, hopping_frequencies=[15, 20, 25])
+data = torch.tensor([1.0, 0.5, 0.3])  # Example data
+encrypted_data = model(data)
+
+!pip install matplotlib
+
+import torch
+import torch.nn as nn
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Define the Frequency Modulation Layer
+class FrequencyModulation(nn.Module):
+    def __init__(self, frequency):
+        super(FrequencyModulation, self).__init__()
+        self.frequency = frequency
+
+    def forward(self, x):
+        return torch.sin(2 * torch.pi * self.frequency * x)
+
+# Define the Encryption Layer
+class EncryptionLayer(nn.Module):
+    def __init__(self, key_frequency):
+        super(EncryptionLayer, self).__init__()
+        self.key_frequency = key_frequency
+
+    def forward(self, x):
+        return torch.sin(2 * torch.pi * (self.key_frequency + x))
+
+# Define the Frequency Hopping Layer
+class FrequencyHopping(nn.Module):
+    def __init__(self, frequencies):
+        super(FrequencyHopping, self).__init__()
+        self.frequencies = frequencies
+
+    def forward(self, x):
+        for freq in self.frequencies:
+            x = torch.sin(2 * torch.pi * freq * x)
+        return x
+
+# Define the Decryption Layer
+class DecryptionLayer(nn.Module):
+    def __init__(self, key_frequency):
+        super(DecryptionLayer, self).__init__()
+        self.key_frequency = key_frequency
+
+    def forward(self, x):
+        return torch.asin(x) / (2 * torch.pi * self.key_frequency)
+
+# Define the FrequencyVPN Model
+class FrequencyVPN(nn.Module):
+    def __init__(self, frequency, key_frequency, hopping_frequencies):
+        super(FrequencyVPN, self).__init__()
+        self.modulation = FrequencyModulation(frequency)
+        self.encryption = EncryptionLayer(key_frequency)
+        self.hopping = FrequencyHopping(hopping_frequencies)
+        self.decryption = DecryptionLayer(key_frequency)
+
+    def forward(self, x):
+        x_modulated = self.modulation(x)
+        x_encrypted = self.encryption(x_modulated)
+        x_hopped = self.hopping(x_encrypted)
+        x_decrypted = self.decryption(x_hopped)
+        return x_modulated, x_encrypted, x_hopped, x_decrypted
+
+# Set the parameters for the model
+frequency = 5           # Modulation frequency
+key_frequency = 10      # Encryption key frequency
+hopping_frequencies = [15, 20, 25]  # Frequencies for hopping
+
+# Create the FrequencyVPN model
+model = FrequencyVPN(frequency, key_frequency, hopping_frequencies)
+
+# Example data (simulating a data packet)
+data = torch.linspace(0, 1, 100)  # 100 data points between 0 and 1
+
+# Pass the data through the model
+x_modulated, x_encrypted, x_hopped, x_decrypted = model(data)
+
+# Convert the torch tensors to numpy arrays for plotting
+data_np = data.numpy()
+x_modulated_np = x_modulated.detach().numpy()
+x_encrypted_np = x_encrypted.detach().numpy()
+x_hopped_np = x_hopped.detach().numpy()
+x_decrypted_np = x_decrypted.detach().numpy()
+
+# Plot the data at each stage
+plt.figure(figsize=(12, 8))
+
+plt.subplot(4, 1, 1)
+plt.plot(data_np, x_modulated_np, label='Modulated Data', color='blue')
+plt.title('Modulated Data')
+plt.grid(True)
+
+plt.subplot(4, 1, 2)
+plt.plot(data_np, x_encrypted_np, label='Encrypted Data', color='green')
+plt.title('Encrypted Data')
+plt.grid(True)
+
+plt.subplot(4, 1, 3)
+plt.plot(data_np, x_hopped_np, label='Frequency Hopped Data', color='red')
+plt.title('Frequency Hopped Data')
+plt.grid(True)
+
+plt.subplot(4, 1, 4)
+plt.plot(data_np, x_decrypted_np, label='Decrypted Data', color='purple')
+plt.title('Decrypted Data')
+plt.grid(True)
+
+plt.tight_layout()
+plt.show()