[1]张 凡*,高仲合,牛 琨.基于 BiGRU-SVM 的网络入侵检测模型[J].计算机技术与发展,2023,33(01):144-149.[doi:10. 3969 / j. issn. 1673-629X. 2023. 01. 022]
　ZHANG Fan*,GAO Zhong-he,NIU Kun.Network Intrusion Detection Model Based on BiGRU-SVM[J].,2023,33(01):144-149.[doi:10. 3969 / j. issn. 1673-629X. 2023. 01. 022]
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基于 BiGRU-SVM 的网络入侵检测模型()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年01期
页码:
144-149
栏目:
网络空间安全
出版日期:
2023-01-10

文章信息/Info

Title:
Network Intrusion Detection Model Based on BiGRU-SVM
文章编号:
1673-629X(2023)01-0144-06
作者:
张 凡* ; 高仲合; 牛 琨
曲阜师范大学 网络空间安全学院,山东 曲阜 273165
Author(s):
ZHANG Fan* ; GAO Zhong-he; NIU Kun
School of Cyber Science and Engineering,Qufu Normal University,Qufu 273165,China
关键词:
网络入侵检测; 时间序列; 双向门控循环单元; 神经网络; 支持向量机
Keywords:
network intrusion detection; time series; bidirectional gated recurrent unit; neural network; support vector machine
分类号:
TP393
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 01. 022
摘要:
随着计算机网络的广泛应用,网络安全问题受到了前所未有的关注。 入侵检测技术是一种主动性安全防护技术,是网络安全管理的重要手段之一。 鉴于神经网络在计算机视觉、自然语言处理等　领域取得的显著成就,针对网络入侵行为具有的不确定性、复杂性、多样性和动态性等特点,提出了一种将门控循环单元(GRU) 应用于入侵检测的模型。 该模型在传统门控循环单元基础上进行　改进,采用双向门控循环单元( BiGRU) 对数据进行正向和逆向学习,然后对学习结果进行线性组合,最后引入支持向量机作为分类器。 采用京都大学蜜罐系统的 2013 年网络流量数据集进行实验　测试,在数据集上实现了网络入侵检测的二分类问题。 实验结果表明,基于支持向量机的双向门控循环单元( BiGRU-SVM) 入侵检测模型误报率降低了 5. 15 百分点,准确率提高了 14. 61 百分点。 表明 BiGRU-SVM 是一种可行且高效的方法,为网络入侵检测领域提供了一种新思路。
Abstract:
With the wide application of computer networks, network security issues have attracted unprecedented attention. Intrusiondetection technology is an active security protection technology,　which is one of the important means of network security management. Inview of the remarkable achievements of neural networks in computer vision,natural language processing and other fields,a gated recurrentunit ( GRU) for intrusion detection is proposed according to the uncertainty,complexity,diversity and dynamics of network intrusion behaviors. The model is improved on the basis of the traditional gated recurrent unit,and the bidirectional gated recurrent unit ( BiGRU) isused for forward and reverse learning of the data,and then the learning results are linearly combined,finally a support vector machine isintroduced as a classifier. The 2013 network traffic data set of the honeypot system of Kyoto University is applied for experimental testingto realize the binary classification problem of network intrusion detection on the data set. The experiments show that the bidirectionalgated recurrent unit ( BiGRU-SVM) intrusion detection model based on support vector machine reduces the false alarm rate by 5. 15%and the accuracy rate increases by 14. 61% . It is concluded that the BiGRU-SVM is a feasible and efficient method,which provides anew idea for the field of network intrusion detection.

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更新日期/Last Update: 2023-01-10