高级python脚本：带有实时可视化的ai驱动网络异常检测器
此脚本组合：
使用scapy的实时网络流量分析。
使用scikit-learn。
基于机器学习的异常检测。使用matplotlib和plotly。
使用大熊猫和电子邮件库的自动报告。>
脚本监视网络流量，检测异常（例如，不寻常的流量模式），并生成实时可视化和电子邮件警报。
import timeimport pandas as pdimport numpy as npfrom scapy.all import sniff, IP, TCPfrom sklearn.ensemble import IsolationForestimport matplotlib.pyplot as pltimport plotly.express as pximport smtplibfrom email.mime.text import MIMETextfrom email.mime.multipart import MIMEMultipartfrom threading import Thread# Global variablesnetwork_data = []anomalies = []model = IsolationForest(contamination=0.01) # Anomaly detection model# Email configurationEMAIL_HOST = 'smtp.gmail.com'EMAIL_PORT = 587EMAIL_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 secondsdef 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:\n\n" 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()
它的工作原理
网络流量捕获：
>脚本使用scapy捕获实时网络流量并提取源ip，目标ip，协议和数据包长度等功能。
>异常检测：
>它使用scikit-learn的隔离森林算法来检测网络流量中的异常模式。
实时可视化：
使用plotly实时可视化检测到的异常。
>
电子邮件警报：
如果检测到异常，则脚本将发送带有详细信息的电子邮件警报。
多线程：
流量捕获和异常检测在单独的线程中运行以提高效率。>
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