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