Network Intrusion Detection Method Based on Attention-BiTCN

doi:10.3969/j.issn.1671-1122.2024.02.014

Abstract

Abstract:

In order to solve the problem of low accuracy of multi-classification in network intrusion detection field, the proposed algorithm analyzed the time series characteristics of network traffic data, an intrusion detection model based on attention mechanism and bi-directional temporal convolutional network (BiTCN) was convolutional neural network. In this model, the data set was pre-processed by heat-only coding and normalization to solve the problem of strong discreteness and different scale of network traffic data, and the pre-processed data were generated into bidirectional sequence by bidirectional sliding window method, attention-bitcn model was used to extract the bidirectional temporal features and integrate them in an additive manner to obtain the fusion features enhanced by temporal information. The proposed model is experimentally verified by the datasets of NSL-KDD and UNSW-NB15, and the accuracy of multiple classification reached 99.70% and 84.07% respectively, which is superior to traditional network intrusion detection algorithms and has more significant detection performance than other deep learning models.

Key words: intrusion detection, attention mechanism, BiTCN, bidirectional sliding window method, fusion feature

CLC Number:

TP309

SUN Hongzhe, WANG Jian, WANG Peng, AN Yulong. Network Intrusion Detection Method Based on Attention-BiTCN[J]. Netinfo Security, 2024, 24(2): 309-318.

Figures/Tables 15

References 32

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实验环境	具体配置
操作系统	Windows 11
CPU	AMD Ryzen 7 5700G 3.8 GHz
内存	16 GB
硬盘	500 GB
实验框架	TensorFlow 2.9.1
开发语言	Python 3.9.7

参数名称	参数设置
损失函数	Crossentropy
优化器	Adam
批量大小	1024
学习率	0.003
迭代次数	25
卷积核	1286432
卷积核尺寸	1?3
膨胀系数	1,2,4
Dropout率	0.5

数据集	注意力位置
数据集	在BiTCN之前	在BiTCN之后
NSL-KDD	99.70%	99.32%
UNSW-NB15	84.07%	82.67%

算法	评价指标
算法	准确率	精确率	召回率	F1-score
TCN	99.40%	99.40%	99.40%	99.40%
BiTCN	99.48%	99.46%	99.48%	99.48%
本文模型	99.70%	99.69%	99.70%	99.69%

算法	评价指标
算法	准确率	精确率	召回率	F1-score
TCN	83.01%	83.78%	83.01%	80.39%
BiTCN	83.20%	84.03%	83.20%	80.59%
本文模型	84.07%	84.26%	84.07%	82.05%