融合WaveNet和BiGRU的网络入侵检测方法

doi:10.12305/j.issn.1001-506X.2022.08.31

Abstract

Abstract:

In order to solve the problem that the accuracy of current intrusion detection algorithms for network intrusion multi classification is generally not high, in view of the time series characteristics of network intrusion data, a network intrusion detection method combining WaveNet and bi-directional gated recurrent unit (BiGRU) is proposed. In order to solve the problem of wide distribution and strong discreteness of the original attack data, the data is encoded and normalized firstly. Then the WaveNet is used for convolution operation to shorten the sequence of the data, and the data features are extracted by the maximum and average pooling parallel method. Finally, BiGRU completes the training of the model and realizes the classification. Based on NSL-KDD, UNSW-NB15 and CIC-IDS2017 data set, a comparative experiment is carried out. The results show that the accuracy of the proposed method for the above data sets can reach 99.62%, 83.98% and 99.86% respectively, which is 0.4%, 1.9% and 0.1% higher than that of CNN-BiLSTM of the same type.

Key words: intrusion detection, bi-directional gated recurrent unit (BiGRU), pooling fusion, feature extraction

CLC Number:

TP393

Zexuan MA, Jin LI, Yanli LU, Chen CHEN. Network intrusion detection method based on WaveNet and BiGRU[J]. Systems Engineering and Electronics, 2022, 44(8): 2652-2660.

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References 32

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模型参数	参数设置
批量大小设置	1 024
损失函数	Categorical_crossentropy
优化器	Nadam
优化器学习率	0.001
Epoch设置	20
扩大卷积层数	4
扩大步长	2\4\8\16
BiGRU单元数	64\128
Dropout率	0.5

数据集	编码方式
数据集	独热编码	标签编码
NSL-KDD	99.62±0.11	98.84±0.17
UNSW-NB15	83.98±0.16	82.77±0.20
CIC-IDS2017	99.86±0.03	99.73±0.08

数据集	池化方式
数据集	平均池化	最大池化	池化融合
NSL-KDD	99.03±0.10	99.22±0.11	99.62±0.11
UNSW-NB15	82.97±0.18	83.26±0.14	83.98±0.16
CIC-IDS2017	99.58±0.04	99.60±0.05	99.86±0.03

算法	评价指标
算法	准确率	精确率	召回率	F1-score
决策树	76.92	71.98	54.52	55.97
随机森林	75.41	84.00	75.41	77.53
K均值聚类	79.34	78.01	79.34	76.28
GRU_RNN	99.13	99.05	99.08	99.06
CNN-BiLSTM	99.22	99.18	99.14	99.15
本文模型	99.62	99.60	99.48	99.40

算法	评价指标
算法	准确率	精确率	召回率	F1-score
决策树	73.37	80.94	73.36	76.30
随机森林	75.41	84.00	75.41	77.53
K均值聚类	70.93	82.42	70.91	76.23
GRU_RNN	81.99	82.59	79.75	81.14
CNN-BiLSTM	82.08	82.68	80.00	81.32
本文模型	83.98	84.13	83.76	83.80

Network intrusion detection method based on WaveNet and BiGRU

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算法	评价指标
算法	准确率	精确率	召回率	F1-score
决策树	96.60	97.62	96.66	97.14
随机森林	98.21	98.58	93.40	95.92
K均值聚类	95.03	96.40	95.21	95.80
GRU_RNN	99.20	99.14	99.18	99.16
CNN-BiLSTM	99.43	99.39	99.42	99.40
本文模型	99.86	99.90	99.82	99.86