import time import pandas as pd import numpy as np from scapy.all import sniff, IP, TCP from sklearn.ensemble import IsolationForest import matplotlib.pyplot as plt import plotly.express as px import smtplib from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart from threading import Thread  # Global variables network_data = [] anomalies = [] model = IsolationForest(contamination=0.01)  # Anomaly detection model  # Email configuration EMAIL_HOST = 'smtp.gmail.com' EMAIL_PORT = 587 EMAIL_USER = 'your_email@gmail.com' EMAIL_PASSWORD = 'your_password' ALERT_EMAIL = 'recipient_email@example.com'  def capture_traffic(packet):     """     Capture network traffic and extract features.     """     if IP in packet:         src_ip = packet[IP].src         dst_ip = packet[IP].dst         protocol = packet[IP].proto         length = len(packet)         timestamp = time.time()          # Append to network data         network_data.append([timestamp, src_ip, dst_ip, protocol, length])  def detect_anomalies():     """     Detect anomalies in network traffic using Isolation Forest.     """     global network_data, anomalies     while True:         if len(network_data) > 100:  # Wait for enough data             df = pd.DataFrame(network_data, columns=['timestamp', 'src_ip', 'dst_ip', 'protocol', 'length'])             X = df[['protocol', 'length']].values              # Train the model and predict anomalies             model.fit(X)             preds = model.predict(X)             df['anomaly'] = preds              # Extract anomalies             anomalies = df[df['anomaly'] == -1]             if not anomalies.empty:                 print("Anomalies detected:")                 print(anomalies)                 send_alert_email(anomalies)                 visualize_anomalies(anomalies)              # Clear old data             network_data = network_data[-100:]  # Keep last 100 entries         time.sleep(10)  # Check for anomalies every 10 seconds  def visualize_anomalies(anomalies):     """     Visualize anomalies using Plotly.     """     fig = px.scatter(anomalies, x='timestamp', y='length', color='protocol',                      title='Network Anomalies Detected')     fig.show()  def send_alert_email(anomalies):     """     Send an email alert with detected anomalies.     """     msg = MIMEMultipart()     msg['From'] = EMAIL_USER     msg['To'] = ALERT_EMAIL     msg['Subject'] = 'Network Anomaly Alert'      body = "The following network anomalies were detected:  "     body += anomalies.to_string()     msg.attach(MIMEText(body, 'plain'))      try:         server = smtplib.SMTP(EMAIL_HOST, EMAIL_PORT)         server.starttls()         server.login(EMAIL_USER, EMAIL_PASSWORD)         server.sendmail(EMAIL_USER, ALERT_EMAIL, msg.as_string())         server.quit()         print("Alert email sent.")     except Exception as e:         print(f"Failed to send email: {e}")  def start_capture():     """     Start capturing network traffic.     """     print("Starting network traffic capture...")     sniff(prn=capture_traffic, store=False)  if __name__ == "__main__":     # Start traffic capture in a separate thread     capture_thread = Thread(target=start_capture)     capture_thread.daemon = True     capture_thread.start()      # Start anomaly detection     detect_anomalies()