基于不完全信息的深度学习网络入侵检测

doi:10.3969/j.issn.1671-1122.2019.06.007

信息网络安全 ›› 2019, Vol. 19 ›› Issue (6): 53-60.doi: 10.3969/j.issn.1671-1122.2019.06.007

基于不完全信息的深度学习网络入侵检测

饶绪黎^1,², 徐彭娜², 陈志德¹, 许力¹()

1. 福建师范大学网络安全与密码技术实验室,福建福州 350007
2. 福州职业技术学院信息技术工程系,福建福州 350108

收稿日期:2019-03-14 出版日期:2019-06-10 发布日期:2020-05-11
作者简介:
作者简介：饶绪黎（1977—）,男,福建,副教授,硕士,主要研究方向为网络通信安全、人工智能应用等;徐彭娜（1992—）,女,福建,硕士,主要研究方向为网络信息安全、生物计算等;陈志德（1976—）,男,福建,教授,博士,主要研究方向为网络空间安全、大数据应用等;许力（1970—）,男,福建,教授,博士,主要研究方向为网络与信息安全、物联网与大数据等。
基金资助:
国家自然科学基金[61841701];福建省教育厅科技项目[JAT160822]

Network Intrusion Detection with Incomplete Information Based on Deep Learning

Xuli RAO^1,², Pengna XU², Zhide CHEN¹, Li XU¹()

1. Laboratory of Network Security and Cryptography, Fujian Normal University, Fuzhou Fujian 350007, China
2. Department of Computer, Fuzhou Polytechnic, Fuzhou Fujian 350108, China

Received:2019-03-14 Online:2019-06-10 Published:2020-05-11

摘要/Abstract

摘要：

在网络数据的采集与传输过程中,经常面临无法完全采集、信息丢失等情况。在不完全信息条件下的网络入侵检测成为网络异常检测的难题。为解决不完全信息入侵检测准确率的问题,结合网络数据的特点,文章提出一种基于不完全信息的深度学习网络入侵检测模型（NIDII-DL）,借助多层感知神经网络构建深度学习模型,实现信息不完全条件下的入侵检测。实验结果表明,NIDII-DL方法在不完全信息条件下的分类精度高于其他算法,且对信息不完全的敏感度更低。

关键词: 不完全信息, 网络入侵检测, 多层感知, 特征量

Abstract:

In the process of network data collection and transmission, the situation of incomplete collection and information loss occurs frequently. Network intrusion detection in the case of incomplete information has become a problem of network anomaly detection. Aiming at solving the problem of incomplete information intrusion detection accuracy, combined with the characteristics of network data, this paper proposes a deep learning network intrusion detection model (NIDLL-DL) based on incomplete information, which uses multi-layer perceptual neural network to construct deep learning model to realize intrusion detection under incomplete information. The experimental results show that the classification accuracy of NIDII-DL under incomplete information is higher than other algorithms, and its sensitivity to incomplete information is lower.

Key words: incomplete information, network intrusion detection, multi-layer perception, feature quantity

中图分类号:

TP309

饶绪黎, 徐彭娜, 陈志德, 许力. 基于不完全信息的深度学习网络入侵检测[J]. 信息网络安全, 2019, 19(6): 53-60.

Xuli RAO, Pengna XU, Zhide CHEN, Li XU. Network Intrusion Detection with Incomplete Information Based on Deep Learning[J]. Netinfo Security, 2019, 19(6): 53-60.

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攻击类型名称	攻击特点
Denial-of-Service (DoS)	非法企图中断或干扰主机或网络的正常运行
Surveillance or probe (Probe)	非法扫描主机或网络,寻找漏洞,搜索系统配置或网络拓扑
Remote to Local (R2L)	远程非授权用户非法获得本地主机的用户特权
User to Root (U2R)	本地非授权用户非法获取本地超级用户或管理员的特权

基于不完全信息的深度学习网络入侵检测

Network Intrusion Detection with Incomplete Information Based on Deep Learning

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