使用 AI 技术实现网络数据包捕获与分析

import pyshark

# 创建一个捕获对象，指定接口名称或文件路径
capture = pyshark.LiveCapture(interface='eth0')

# 开始实时捕获
for packet in capture.sniff_continuously():
    try:
        # 打印每一层的信息
        print(f'Packet: {packet}')
    except AttributeError as e:
        # 处理某些情况下可能出现的属性错误
        pass

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import pandas as pd

df = pd.read_csv('processed_packets.csv')
X, y = df.drop(['label'], axis=1).values, df['label'].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2)

clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)

print(clf.score(X_test, y_test))

# 示例：使用Scapy解析三次握手特征
from scapy.all import *

def extract_features(pcap):
    handshake_seq = []
    for pkt in pcap:
        if TCP in pkt and pkt[TCP].flags == 0x02:  # SYN检测
            handshake_seq.append({'time': pkt.time, 'src': pkt[IP].src})
        elif TCP in pkt and pkt[TCP].flags == 0x12:  # SYN-ACK检测
            handshake_seq.append({'window': pkt[TCP].window})
    return pd.DataFrame(handshake_seq)  # 转换为AI可处理格式

model = Sequential()
model.add(LSTM(50, input_shape=(10, 1)))  # 10个时间步
model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')