基于深度学习的物联网终端设备接入认证方法

doi:10.3969/j.issn.1671-1122.2020.11.009

信息网络安全 ›› 2020, Vol. 20 ›› Issue (11): 67-74.doi: 10.3969/j.issn.1671-1122.2020.11.009

基于深度学习的物联网终端设备接入认证方法

程洋(), 雷敏, 罗群

北京邮电大学网络空间安全学院,北京 100876

收稿日期:2020-09-04 出版日期:2020-11-10 发布日期:2020-12-31
通讯作者: 程洋 E-mail:chengyangmc@bupt.edu.cn
作者简介:程洋（1994—）,男,山东,硕士研究生,主要研究方向为物联网与嵌入式系统|雷敏（1979—）,男,江西,副教授,博士,主要研究方向为网络安全|罗群（1959—）,女,江西,教授,博士,主要研究方向为网络安全
基金资助:
国家重点研发计划(2018YFB0803602)

Access Authentication Method for IoT Terminal Devices Based on Deep Learning

CHENG Yang(), LEI Min, LUO Qun

School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2020-09-04 Online:2020-11-10 Published:2020-12-31
Contact: CHENG Yang E-mail:chengyangmc@bupt.edu.cn

摘要/Abstract

摘要：

目前基于数据流量的被动设备指纹的识别方法未考虑数据包之间时间到达顺序,无法提取出其深层特征。文章提出一种基于深度学习模型的物联网设备接入认证方法。该方法从设备入网配置阶段产生的数据包中提取设备特征构建被动式设备指纹,采用双向长短期记忆网络（Bi-LSTM）提取设备指纹中的深层特征。为提高设备识别能力,文章使用固定窗口滑动机制以及SMOTE算法从特征提取和向量化处理两个阶段增强数据,解决数据不平衡问题并去除干扰向量。仿真结果表明,该方法可有效识别设备身份,与传统机器学习和深度学习相比,文章提出的方法设备识别准确率提升了6%。

关键词: 物联网终端设备, 接入认证, 深度学习, 设备指纹识别

Abstract:

At present, the fingerprint identification methods of passive devices based on data flow do not consider the time arrival order between packets, also can not extract its deep features. This paper proposes an access authentication method for Internet of things based on deep learning. This method extracts device features from the data packets generated in the configuration phase of device access to construct passive device fingerprints, uses Bi-LSTM to extract deep features from the device fingerprints. In order to improve the equipment recognition ability, this paper uses fixed window sliding mechanism and smote algorithm to enhance the data from feature extraction and vectorization processing, so as to solve the problem of data imbalance and remove the interference vector. The simulation results show that the method can effectively identify the device identity. Compared with the traditional machine learning and deep learning, the accuracy of the proposed method is improved by 6%.

Key words: IoT terminal device, access authentication, deep learning, device fingerprint recognition

中图分类号:

TP309

程洋, 雷敏, 罗群. 基于深度学习的物联网终端设备接入认证方法[J]. 信息网络安全, 2020, 20(11): 67-74.

CHENG Yang, LEI Min, LUO Qun. Access Authentication Method for IoT Terminal Devices Based on Deep Learning[J]. Netinfo Security, 2020, 20(11): 67-74.

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参考文献 15

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特征种类	类型	特征
协议类型特征	数据链路层协议	ARP
	网络层协议	IP/ICMP/ICMPv6/EAPoL/IGMP
	传输层协议	TCP/UDP
	应用层协议	HTTP/HTTPS/DHCP/BOOTP/SSDP/DNS/MDNS/NTP/SMB/SSH/AFP
	IP头选项	Padding/RouterAlert
统计类型特征	包内容	Raw data/Size（int）
	端口级别	Source(int)/Destination(int)
	时间	IAT（double）

	M Accuracy	M Precision	M Recall	M F1-Score
SVM	0.781538	0.911531	0.631538	0.726646
Adaboost	0.673462	0.768438	0.540769	0.590631
Randomforest	0.75	0.865915	0.586154	0.673838
LSTM	0.788077	0.869438	0.695385	0.764077
Bi-LSTM	0.877462	0.886285	0.831954	0.85826

	M Accuracy	M Precision	M Recall	M F1-Score
无SMOTE	0.858077	0.910154	0.690692	0.78538
SMOTE	0.877462	0.886285	0.831954	0.85766

基于深度学习的物联网终端设备接入认证方法

Access Authentication Method for IoT Terminal Devices Based on Deep Learning

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