宝应做网站,企业宣传册模板,宠物网站建设,建站公司兴田德润在哪里之前用NSL-KDD数据集做入侵检测的项目是#xff1a; 【1】https://qq742971636.blog.csdn.net/article/details/137082925 【2】https://qq742971636.blog.csdn.net/article/details/137170933
有人问我是不是可以改代码#xff0c;我说可以。
训练
我将NSL_KDD_Final_1.i…之前用NSL-KDD数据集做入侵检测的项目是 【1】https://qq742971636.blog.csdn.net/article/details/137082925 【2】https://qq742971636.blog.csdn.net/article/details/137170933
有人问我是不是可以改代码我说可以。
训练
我将NSL_KDD_Final_1.ipynb的代码改为了train.py效果还不错。
前处理也是需要onehot编码
也会归一化 模型
# 双向RNN
batch_size 32
model Sequential()
model.add(Convolution1D(64, kernel_size122, paddingsame, activationrelu, input_shape(122, 1)))
model.add(MaxPooling1D(pool_size(5)))
model.add(BatchNormalization())
model.add(Bidirectional(LSTM(64, return_sequencesFalse)))
model.add(Reshape((128, 1), input_shape(128,)))
model.add(MaxPooling1D(pool_size(5)))
model.add(BatchNormalization())
model.add(Bidirectional(LSTM(128, return_sequencesFalse)))
model.add(Dropout(0.5))
model.add(Dense(5))
model.add(Activation(softmax))
model.compile(losscategorical_crossentropy, optimizeradam, metrics[accuracy])
print(model.summary())Model: sequential
┌─────────────────────────────────┬────────────────────────┬───────────────┐
│ Layer (type) │ Output Shape │ Param # │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d (Conv1D) │ (None, 122, 64) │ 7,872 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling1d (MaxPooling1D) │ (None, 24, 64) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization │ (None, 24, 64) │ 256 │
│ (BatchNormalization) │ │ │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ bidirectional (Bidirectional) │ (None, 128) │ 66,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ reshape (Reshape) │ (None, 128, 1) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling1d_1 (MaxPooling1D) │ (None, 25, 1) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_1 │ (None, 25, 1) │ 4 │
│ (BatchNormalization) │ │ │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ bidirectional_1 (Bidirectional) │ (None, 256) │ 133,120 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout) │ (None, 256) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense) │ (None, 5) │ 1,285 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ activation (Activation) │ (None, 5) │ 0 │
└─────────────────────────────────┴────────────────────────┴───────────────┘Total params: 208,585 (814.79 KB)Trainable params: 208,455 (814.28 KB)Non-trainable params: 130 (520.00 B)在训练过程中效果已经不错
输入特征
list(combined_data_X.columns)
Out[4]:
[duration,src_bytes,dst_bytes,land,wrong_fragment,urgent,hot,num_failed_logins,logged_in,num_compromised,root_shell,su_attempted,num_root,num_file_creations,num_shells,num_access_files,num_outbound_cmds,is_host_login,is_guest_login,count,srv_count,serror_rate,srv_serror_rate,rerror_rate,srv_rerror_rate,same_srv_rate,diff_srv_rate,srv_diff_host_rate,dst_host_count,dst_host_srv_count,dst_host_same_srv_rate,dst_host_diff_srv_rate,dst_host_same_src_port_rate,dst_host_srv_diff_host_rate,dst_host_serror_rate,dst_host_srv_serror_rate,dst_host_rerror_rate,dst_host_srv_rerror_rate,protocol_type_icmp,protocol_type_tcp,protocol_type_udp,service_IRC,service_X11,service_Z39_50,service_aol,service_auth,service_bgp,service_courier,service_csnet_ns,service_ctf,service_daytime,service_discard,service_domain,service_domain_u,service_echo,service_eco_i,service_ecr_i,service_efs,service_exec,service_finger,service_ftp,service_ftp_data,service_gopher,service_harvest,service_hostnames,service_http,service_http_2784,service_http_443,service_http_8001,service_imap4,service_iso_tsap,service_klogin,service_kshell,service_ldap,service_link,service_login,service_mtp,service_name,service_netbios_dgm,service_netbios_ns,service_netbios_ssn,service_netstat,service_nnsp,service_nntp,service_ntp_u,service_other,service_pm_dump,service_pop_2,service_pop_3,service_printer,service_private,service_red_i,service_remote_job,service_rje,service_shell,service_smtp,service_sql_net,service_ssh,service_sunrpc,service_supdup,service_systat,service_telnet,service_tftp_u,service_tim_i,service_time,service_urh_i,service_urp_i,service_uucp,service_uucp_path,service_vmnet,service_whois,flag_OTH,flag_REJ,flag_RSTO,flag_RSTOS0,flag_RSTR,flag_S0,flag_S1,flag_S2,flag_S3,flag_SF,flag_SH]类别输出 Class Normal 77232 DoS 53387 Probe 14077 R2L 3702 U2R 119 Name: count, dtype: int64
推理
推理代码
# 加载测试集
import numpy as np
import pandas as pd
from keras import Sequential
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Embedding
from keras.layers import LSTM, SimpleRNN, GRU, Bidirectional, BatchNormalization, Convolution1D, MaxPooling1D, Reshape, \GlobalAveragePooling1D
from keras.utils import to_categorical
import sklearn.preprocessing
from sklearn import metrics
from keras.models import load_model# 双向RNN
batch_size 32
model Sequential()
model.add(Convolution1D(64, kernel_size122, paddingsame, activationrelu, input_shape(122, 1)))
model.add(MaxPooling1D(pool_size(5)))
model.add(BatchNormalization())
model.add(Bidirectional(LSTM(64, return_sequencesFalse)))
model.add(Reshape((128, 1), input_shape(128,)))
model.add(MaxPooling1D(pool_size(5)))
model.add(BatchNormalization())
model.add(Bidirectional(LSTM(128, return_sequencesFalse)))
model.add(Dropout(0.5))
model.add(Dense(5))
model.add(Activation(softmax))
model.compile(losscategorical_crossentropy, optimizeradam, metrics[accuracy])
print(model.summary())
# 加载之前保存的模型
# model load_model(oldNSL_KDD.h5)
# 在确保模型架构完全一致的情况下只加载权重
model.load_weights(oldNSL_KDD.h5)data pd.read_excel(combined_data_X.xlsx, index_col0)
input_features data.iloc[0, :].values# 因为模型期望的输入形状是 (batch_size, timesteps, features_per_timestep)
# 我们需要将输入数据reshape成 (1, 122, 1) 形状这里 1 是batch size
input_features input_features.reshape(1, 122, 1)# 使用模型进行推理
predictions model.predict(input_features)# 打印预测的概率predictions将是一个形状为(1, 5)的数组包含5个输出类别的概率
print(predictions)# Class
# Normal 77232
# DoS 53387
# Probe 14077
# R2L 3702
# U2R 119
# Name: count, dtype: int64# 输出top1的类别和概率
top1_class np.argmax(predictions)
top1_probability predictions[0, top1_class]
print(fTop1 class: {top1_class}, probability: {top1_probability})
推理结果
D:\ProgramData\miniconda3\envs\py310\python.exe E:\workcode\pytestx\pythonProject\ruqinx\b_lstm\val.py
2024-04-16 17:02:44.386562: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS0.
2024-04-16 17:02:45.090275: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS0.
D:\ProgramData\miniconda3\envs\py310\lib\site-packages\keras\src\layers\convolutional\base_conv.py:99: UserWarning: Do not pass an input_shape/input_dim argument to a layer. When using Sequential models, prefer using an Input(shape) object as the first layer in the model instead.super().__init__(
2024-04-16 17:02:46.745679: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
D:\ProgramData\miniconda3\envs\py310\lib\site-packages\keras\src\layers\reshaping\reshape.py:39: UserWarning: Do not pass an input_shape/input_dim argument to a layer. When using Sequential models, prefer using an Input(shape) object as the first layer in the model instead.super().__init__(**kwargs)
Model: sequential
┌─────────────────────────────────┬────────────────────────┬───────────────┐
│ Layer (type) │ Output Shape │ Param # │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d (Conv1D) │ (None, 122, 64) │ 7,872 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling1d (MaxPooling1D) │ (None, 24, 64) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization │ (None, 24, 64) │ 256 │
│ (BatchNormalization) │ │ │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ bidirectional (Bidirectional) │ (None, 128) │ 66,048 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ reshape (Reshape) │ (None, 128, 1) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling1d_1 (MaxPooling1D) │ (None, 25, 1) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_1 │ (None, 25, 1) │ 4 │
│ (BatchNormalization) │ │ │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ bidirectional_1 (Bidirectional) │ (None, 256) │ 133,120 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout) │ (None, 256) │ 0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense) │ (None, 5) │ 1,285 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ activation (Activation) │ (None, 5) │ 0 │
└─────────────────────────────────┴────────────────────────┴───────────────┘Total params: 208,585 (814.79 KB)Trainable params: 208,455 (814.28 KB)Non-trainable params: 130 (520.00 B)
None
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
[[1.7173363e-06 9.9280131e-01 1.5588279e-05 7.0899338e-03 9.1460090e-05]]
Top1 class: 1, probability: 0.992801308631897Process finished with exit code 0
工程介绍 gradio前端展示界面 代码获取
https://docs.qq.com/sheet/DUEdqZ2lmbmR6UVdU?tabBB08J2
