[1]李奕蒙,高玉琢*.基于膨胀卷积与BiLSTM的入侵检测模型[J].计算机技术与发展,2024,34(07):87-92.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0099]
　LI Yi-meng,GAO Yu-zhuo*.A Network Intrusion Detection Model Based on Dilated Convolution Network and BiLSTM[J].,2024,34(07):87-92.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0099]
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基于膨胀卷积与BiLSTM的入侵检测模型

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年07期
页码:
87-92
栏目:
网络空间安全
出版日期:
2024-07-10

文章信息/Info

Title:
A Network Intrusion Detection Model Based on Dilated Convolution Network and BiLSTM
文章编号:
1673-629X(2024)07-0087-06
作者:
李奕蒙; 高玉琢*
宁夏大学 信息工程学院,宁夏 银川 750021
Author(s):
LI Yi-meng; GAO Yu-zhuo*
School of Information Engineering,Ningxia University,Yinchuan 750021,China
关键词:
网络安全; 入侵检测; 膨胀卷积网络; 双向长短记忆网络; 特征提取
Keywords:
network security; intrusion detection; dilated convolution network; bidirectional long short memory network; feature extraction
分类号:
TP393.0
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0099
摘要:
针对基于深度学习的入侵检测系统存在局部特征提取效果不佳,提取维度单一,预测精度有待提高和模型计算开 销过高等问题,提出了一种基于膨胀卷积网络(Dilated Convolution Network,DCN)与双向长短记忆网络(Bidirectional Long Short Memory Network ,BiLSTM)的入侵检测模型。 首先使用改进的三层膨胀卷积神经网络(Dilated Convolution Network,DCN)结构进行局部特征提取,解决了局部特征提取效果不佳、提取维度单一的问题;其次使用将膨胀卷积与 BiLSTM 相结合的办法解决预测精度有待提高的问题;最后通过在模型中引入分组卷积技术使模型轻量化,解决了计算开销过高的问题。 在公开数据集 CICIDS2017 上进行了实验,经过对比传统模型以及现有的入侵检测方法表明,所提模型拥有较好的性能。 模型预测准确率、召回率、F1 值较高,证明了其有效性和可行性。
Abstract:
Aiming at the problems of the intrusion detection system based on deep learning,such as poor local feature extraction effect,single extraction dimension,prediction accuracy to be improved and high cost of model calculation,an intrusion detection model based on Dilated Convolutional Network (DCN) and Bidirectional Long Short Memory Network (BiLSTM) is proposed. Firstly,an improved three-layer DCN structure was used for local feature extraction,which solved the problem of poor local feature extraction performance and single extraction dimension. Secondly,the method combining expansion convolution with BiLSTM was used to solve the problem that the prediction accuracy needs to be improved. Finally,grouping convolution technology was introduced into the model to make the model lightweight, which solves the problem of high computational cost. The experiments were conducted on the public dataset CICIDS2017. After comparing traditional models with existing intrusion detection methods, the proposed model has excellent performance. The high prediction accuracy,recall rate,and F1 value of the proposed model demonstrate its effectiveness and feasibility.

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更新日期/Last Update: 2024-07-10