from scapy.all import sniff, IP, TCP
import torch
import torch.nn as nn
import numpy as np
import random

# Network Behavior Analyzer with Sparkle Effect
class NetworkBehaviorAnalyzer:
def __init__(self):
self.history = []

def update_history(self, packet_feature):
self.history.append(packet_feature)

def analyze_recent_behavior(self):
# Analyze for sparkly network behavior
return np.mean(self.history[-10:], axis=0) if len(self.history) > 10 else np.zeros(10)

def is_network_sparkling(self):
# Determine if the network behavior is 'sparkling'
recent_behavior = self.analyze_recent_behavior()
return np.any(recent_behavior > threshold) # Placeholder for threshold

# Enhanced Packet Processor with Sparkle Detection
class EnhancedPacketProcessor(PacketProcessor):
def __init__(self, model, behavior_analyzer):
super().__init__(model)
self.behavior_analyzer = behavior_analyzer

def decide(self, analysis_result):
sparkling = self.behavior_analyzer.is_network_sparkling()
decision = 'monitor' if sparkling else random.choice(['alert', 'ignore'])
return decision, analysis_result

# Advanced AI Model for Network Analysis
class AdvancedAIModel(nn.Module):
def __init__(self):
super(AdvancedAIModel, self).__init__()
self.layers = nn.Sequential(
nn.Linear(20, 40),
nn.ReLU(),
nn.Linear(40, 20),
nn.ReLU(),
nn.Linear(20, 10)
)

def forward(self, x):
return self.layers(x)

# Instantiate components
ai_model = AdvancedAIModel()
behavior_analyzer = NetworkBehaviorAnalyzer()
processor = EnhancedPacketProcessor(ai_model, behavior_analyzer)

# Function to extract features from packets
def extract_packet_features(packet):
features = [packet[IP].len, packet[IP].ttl] if IP in packet else [0, 0]
features += [packet[TCP].window, packet[TCP].flags.value] if TCP in packet else [0, 0]
return np.array(features + [0]*(10-len(features)))

# Modified packet callback for Squiddy Ghost
def packet_callback(packet):
features = extract_packet_features(packet)
behavior_analyzer.update_history(features)
tensor_features = torch.tensor(features, dtype=torch.float32)
analysis = processor.analyze(tensor_features)
decision, analysis_result = processor.decide(analysis)
print(f"Squiddy Ghost: Decision - {decision}, Analysis - {analysis_result}")

# Main function for Squiddy Ghost AI
def main():
print("Squiddy Ghost AI is sparkling and ready!")
sniff(prn=packet_callback, count=10)

if __name__ == "__main__":
main()