five modes duplication

test4.py

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+# Name:fang xiaoyu
+# Time: 2023/3/11 18:43
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import dpkt
+import socket
+import struct
+import binascii
+from scapy.all import *
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import cross_val_score
+#import tshark
+import scapy
+
+# 从pcap文件中读取流量数据
+def read_pcap(filename):
+    packets = rdpcap(filename)
+    flows = {}
+    for packet in packets:
+        if packet.haslayer(TCP):
+            src_ip = packet[IP].src
+            dst_ip = packet[IP].dst
+            src_port = packet[TCP].sport
+            dst_port = packet[TCP].dport
+            key = (src_ip, dst_ip, src_port, dst_port)
+            if key not in flows:
+                flows[key] = [packet]
+            else:
+                flows[key].append(packet)
+    return flows
+
+# 提取流量数据的特征
+def extract_features(flow):
+    features = []
+    total_len = 0
+    total_pkts = len(flow)
+    start_time = flow[0].time
+    end_time = flow[-1].time
+    for packet in flow:
+        total_len += len(packet)
+    duration = end_time - start_time
+    features.append(total_len)
+    features.append(total_pkts)
+    features.append(duration)
+    return features
+
+# 将特征向量转换为numpy数组
+def vectorize_data(data):
+    return np.array(data)
+
+# 读取VPN和non-VPN流量数据
+vpn_traffic = read_pcap('vpn_traffic.pcap')
+nonvpn_traffic = read_pcap('nonvpn_traffic.pcap')
+
+# 提取VPN和non-VPN流量的特征
+vpn_traffic = [extract_features(flow) for flow in vpn_traffic.values()]
+nonvpn_traffic = [extract_features(flow) for flow in nonvpn_traffic.values()]
+
+# 将VPN和non-VPN流量数据转换为numpy数组
+vpn_traffic = vectorize_data(vpn_traffic)
+nonvpn_traffic = vectorize_data(nonvpn_traffic)
+
+# 将VPN和non-VPN流量数据合并
+X = np.concatenate((vpn_traffic, nonvpn_traffic))
+y = np.concatenate((np.ones(len(vpn_traffic)), np.zeros(len(nonvpn_traffic))))
+
+# 使用交叉验证选择最佳的K值
+cv_scores = []
+for k in range(1, 31):
+    knn = KNeighborsClassifier(n_neighbors=k)
+    scores = cross_val_score(knn, X, y, cv=5, scoring='accuracy')
+    cv_scores.append(scores.mean())
+
+# 可视化交叉验证结果
+plt.plot(range(1, 31), cv_scores)
+plt.xlabel('K')
+plt.ylabel('Accuracy')
+plt.show()
+
+# 使用最佳的K值进行模型训练和预测
+best_k = np.argmax(cv_scores) + 1
+knn = KNeighborsClassifier(n_neighbors=best_k)
+knn.fit(X, y)
+
+# 对新数据进行预测
+new_data = read_pcap('new_traffic.pcap')
+new_data = [extract_features(flow) for flow in new_data.values()]
+new_data = vectorize_data(new_data)
+prediction = knn.predict(new_data)
+print(prediction)