如何使用tensorflow_io的IODataset?
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我正在尝试编写一个程序,该程序可以使用恶意pcap文件作为数据集,并预测其他pcaps文件中是否包含恶意数据包。在仔细研究过Tensorflow重复项之后,我找到了TensorIO,但是我不知道如何使用数据集创建模型并进行预测。
这是我的代码:
%tensorflow_version 2.x
import tensorflow as tf
import numpy as np
from tensorflow import keras
try:
import tensorflow_io as tfio
import tensorflow_datasets as tfds
except:
!pip install tensorflow-io
!pip install tensorflow-datasets
import tensorflow_io as tfio
import tensorflow_datasets as tfds
# print(tf.__version__)
dataset = tfio.IODataset.from_pcap("dataset.pcap")
print(dataset) # <PcapIODataset shapes: ((), ()), types: (tf.float64, tf.string)>
(使用Google Collab)
我尝试在线寻找答案,但找不到任何答案。
我已经下载了两个pcap文件并将其串联。后来我提取了packet_timestamp和packet_data。要求您根据需要预处理packet_data。如果要添加任何标签,则可以将其添加到训练数据集中(在下面的模型示例中,我创建了一个全零的虚拟标签并作为列添加)。如果在文件中,则可以将它们zip转换为pcap文件。 Model.fit
和Model.evaluate
只需传递(特征,标签)对的数据集:
以下是packet_data预处理的一个示例-可以像if packet_data is valid then labels = valid else malicious
一样进行修改。
%tensorflow_version 2.x
import tensorflow as tf
import tensorflow_io as tfio
import numpy as np
# Create an IODataset from a pcap file
first_file = tfio.IODataset.from_pcap('/content/fuzz-2006-06-26-2594.pcap')
second_file = tfio.IODataset.from_pcap(['/content/fuzz-2006-08-27-19853.pcap'])
# Concatenate the Read Files
feature = first_file.concatenate(second_file)
# List for pcap
packet_timestamp_list = []
packet_data_list = []
# some dummy labels
labels = []
packets_total = 0
for v in feature:
(packet_timestamp, packet_data) = v
packet_timestamp_list.append(packet_timestamp.numpy())
packet_data_list.append(packet_data.numpy())
labels.append(0)
if packets_total == 0:
assert np.isclose(
packet_timestamp.numpy()[0], 1084443427.311224, rtol=1e-15
) # we know this is the correct value in the test pcap file
assert (
len(packet_data.numpy()[0]) == 62
) # we know this is the correct packet data buffer length in the test pcap file
packets_total += 1
assert (
packets_total == 43
) # we know this is the correct number of packets in the test pcap file
[下面是在模型中使用的示例-该模型将无法工作,因为我尚未处理字符串类型的packet_data。按照您的要求进行预处理,然后在模型中使用。
%tensorflow_version 2.x
import tensorflow as tf
import tensorflow_io as tfio
import numpy as np
# Create an IODataset from a pcap file
first_file = tfio.IODataset.from_pcap('/content/fuzz-2006-06-26-2594.pcap')
second_file = tfio.IODataset.from_pcap(['/content/fuzz-2006-08-27-19853.pcap'])
# Concatenate the Read Files
feature = first_file.concatenate(second_file)
# List for pcap
packet_timestamp = []
packet_data = []
# some dummy labels
labels = []
# add 0 as label. You can use your actual labels here
for v in feature:
(timestamp, data) = v
packet_timestamp.append(timestamp.numpy())
packet_data.append(data.numpy())
labels.append(0)
## Do the preprocessing of packet_data here
# Add labels to the training data
# Preprocess the packet_data to convert string to meaningful value and use here
train_ds = tf.data.Dataset.from_tensor_slices(((packet_timestamp,packet_data), labels))
# Set the batch size
train_ds = train_ds.shuffle(5000).batch(32)
##### PROGRAM WILL RUN SUCCESSFULLY TILL HERE. TO USE IN THE MODEL DO THE PREPROCESSING OF PACKET DATA AS EXPLAINED ###
# Have defined some simple model
model = tf.keras.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(100),
tf.keras.layers.Dense(10)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
model.fit(train_ds, epochs=2)
希望这能回答您的问题。祝您学习愉快。
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