| 基于GRU与特征嵌入的网络入侵检测 |
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| 引用本文： | 颜亮,姬少培,刘栋,谢建武. 基于GRU与特征嵌入的网络入侵检测[J]. 应用科学学报, 2021, 39(4): 559-568. DOI: 10.3969/j.issn.0255-8297.2021.04.004 |
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| 作者姓名： | 颜亮 姬少培 刘栋 谢建武 |
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| 作者单位： | 中国电子科技集团公司 第三十研究所, 四川 成都 610041 |
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| 基金项目： | 四川省重大科技项目基金（No.2017GZDZX0002）资助 |
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| 摘 要： | 当前基于神经网络的入侵检测方法并没有将数据分类信息考虑在内,无法有效利用网络流量数据的时序信息,为此将门控循环单元(gated recurrent unit,GRU)和基于分类信息的特征嵌入技术结合起来,构建了基于GRU与特征嵌入的网络入侵检测模型.利用UNSW-NB15数据集进行模型仿真实验,结果表明该模型提高了对入...
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| 关 键 词： | 网络入侵检测 机器学习 门循环单元 特征嵌入 |
| 收稿时间： | 2020-08-21 |
Network Intrusion Detection Based on GRU and Feature Embedding |
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| YAN Liang,JI Shaopei,LIU Dong,XIE Jianwu. Network Intrusion Detection Based on GRU and Feature Embedding[J]. Journal of Applied Sciences, 2021, 39(4): 559-568. DOI: 10.3969/j.issn.0255-8297.2021.04.004 |
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| Authors: | YAN Liang JI Shaopei LIU Dong XIE Jianwu |
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| Affiliation: | No. 30 Research Institute, China Electronics Technology Corporation, Chengdu 610041, Sichuan, China |
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| Abstract: | The existing intrusion detection methods based on neural network have not taken data classification information into consideration yet, thus, the timing information of network traffic data are not used effectively. In this paper, we propose network intrusion detection models based on gated recurrent unit (GRU) in combination with embedding technique of categorical information. Simulation experiments on the models are carried out with UNSW-NB15, which is a comprehensive network traffic dataset. Experimental results show that the proposed models not only improve the detection rate of intrusion attacks, but also provide a new way for intrusion detection in case of processing large-scale data. |
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| Keywords: | network intrusion detection machine learning gate recurrent unit (GRU) feature embedding |
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