协议预测实现中的LSTM/GRU模型准确性问题

我有一个特定的模型要创建。我将在下面详细介绍

1. 我有一个wireshark cap文件,从中我生成了一个1.4行的csv文件(不是整个cap文件而是它的一个示例)
2. 我想在我的模型中使用的信息是 源、目标、协议 列和 LSTM 我实现的模型应该理解源/目的地/协议字段和 对协议字段进行预测 .
3. 问题来了。。。协议字段中的协议为 不平衡的 约有100万个TCP和20万个UDP实例,其余为其他协议
4. 我创建的模型达到了0.8-0.85的精度,这仅仅是TCP预测

我试着调整 序列长度、时期、批量大小只占数据集的一小部分(这会导致更糟糕的预测) 我还尝试对协议进行一些权重平衡,default_weight库使每个协议的精度为0.3/0.4,但由于示例的主要协议是TCP,因此总体精度为0.2

任何关于我可以更改以提高欠采样协议准确性的建议都是非常受欢迎的!

import pandas as pd
import numpy as np
from sklearn.model_selection import TimeSeriesSplit
from sklearn.feature_extraction import FeatureHasher
from sklearn.preprocessing import LabelEncoder
from keras.models import Sequential
from keras.layers import LSTM, Dense
from keras.utils import to_categorical
import matplotlib.pyplot as plt


# Load and preprocess the data
data = pd.read_csv('paketa.csv')
data = data.dropna()
data = data[(data['Protocol'] != 'IPv6') & (data['Protocol'] != 'MPTCP')]
unique_classes = data['Protocol'].unique()
print("Unique Class Indices:", unique_classes)
# Extract subset of the data
#subset_start_index = 0
#subset_end_index = 200000
#subset_data = data.iloc[subset_start_index:subset_end_index]

# Preprocess categorical features using FeatureHasher
# Convert 'Source' and 'Destination' to lists of strings
source_strings = data['Source'].astype(str).apply(lambda x: x.split('.')).values
destination_strings = data['Destination'].astype(str).apply(lambda x: x.split('.')).values

hasher_source = FeatureHasher(n_features=10, input_type='string')
hashed_source = hasher_source.transform(source_strings)
hashed_destination = hasher_source.transform(destination_strings)

# One-hot encode the 'Protocol' feature
protocol_onehot = pd.get_dummies(data['Protocol'])

# Combine the preprocessed features
hashed_features_array = np.concatenate([hashed_source.toarray(), hashed_destination.toarray(), protocol_onehot], axis=1)

# Continue with the rest of your code
sequence_length = 10
sequences = []
targets = []

for i in range(len(hashed_features_array) - sequence_length):
    seq = hashed_features_array[i:i+sequence_length]
    target = data.iloc[i+sequence_length]['Protocol']
    sequences.append(seq)
    targets.append(target)

X = np.array(sequences)
y = np.array(targets)
print(X)
print(X.shape)
print(y.shape)

# Convert target labels to numerical indices
unique_labels = np.unique(y)
label_encoder = LabelEncoder()
label_encoder.fit(unique_labels)
y_indices = np.array([label_encoder.transform([label])[0] for label in y])

# Convert target indices to one-hot encoded vectors
y_onehot = to_categorical(y_indices, num_classes=len(unique_labels))

# Build LSTM model
num_classes = len(unique_labels)
num_features = hashed_features_array.shape[1]

model = Sequential()
model.add(LSTM(128, input_shape=(sequence_length, num_features)))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Use TimeSeriesSplit for train-test split
tscv = TimeSeriesSplit(n_splits=2)

for train_index, test_index in tscv.split(X):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y_onehot[train_index], y_onehot[test_index]

    # Train the model without class weights
    model.fit(X_train, y_train, epochs=5, batch_size=32)

    # Evaluate the model
    loss, accuracy = model.evaluate(X_test, y_test)
    print("Model Results:")
    print(f"Loss: {loss}, Accuracy: {accuracy}")