Delete test4.py

test4.py

@@ -1,91 +0,0 @@
-# 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)