基于博弈优化边缘学习的物联网入侵检测研究

doi:10.11959/j.issn.2096-3750.2021.00226

物联网学报 ›› 2021, Vol. 5 ›› Issue (2): 37-47.doi: 10.11959/j.issn.2096-3750.2021.00226

• 专题：物联网边缘智能与雾计算技术 • 上一篇下一篇

基于博弈优化边缘学习的物联网入侵检测研究

梁浩然¹, 伍军¹, 赵程程¹^,², 李建华¹

¹ 上海交通大学，上海 200240
² 室兰工业大学，日本室兰 050-8585

修回日期:2021-02-09 出版日期:2021-06-30 发布日期:2021-06-01
作者简介:梁浩然（1989- ），男，上海交通大学网络安全技术研究院博士生，主要研究方向为物联网安全、联邦学习等
伍军（1979- ），男，上海交通大学网络安全技术研究院副院长、教授、博士生导师，主要研究方向为物联网安全、新型网络安全技术、边缘智能、区块链等
赵程程（1993- ），女，上海交通大学网络安全技术研究院博士生，日本室兰工业大学公派联合培养博士生，主要研究方向为物联网技术、信息中心网络等
李建华（1965- ），男，上海交通大学网络安全技术研究院院长、教授、博士生导师，主要研究方向为信息内容安全技术、态势感知、工控网络安全等
基金资助:
国家自然科学基金资助项目(U20B2048);国家自然科学基金资助项目(61972255)

Leveraging edge learning and game theory for intrusion detection in Internet of things

Haoran LIANG¹, Jun WU¹, Chengcheng ZHAO¹^,², Jianhua LI¹

¹ Shanghai Jiao Tong University, Shanghai 200240, China
² Muroran Institute of Technology, Muroran 050-8585, Japan

Revised:2021-02-09 Online:2021-06-30 Published:2021-06-01
Supported by:
The National Natural Science Foundation of China(U20B2048);The National Natural Science Foundation of China(61972255)

摘要/Abstract

摘要：

随着 5G 的商用和 6G 开始布局，海量物联网设备正在加速接入互联网，为新一代信息物理融合系统提供决策数据。物联网设备的高度异构及分布式特性使得物联网面临复杂威胁，这些威胁可使信息物理融合系统的关键决策失效。传统技术难以在保护节点隐私的前提下进行入侵检测且容易形成单点故障，同时缺乏协同入侵检测激励机制。因此，基于博弈优化边缘学习，研究了面向物联网的入侵检测系统。基于联邦学习在网络边缘构建了分布式隐私保护物联网入侵检测框架。在此基础上，基于多主多从博弈优化边缘学习过程，激励可信的入侵检测服务器及边缘设备参与边缘联邦学习。仿真实验证明了所提出的联网入侵检测系统的安全性和有效性。

关键词: 物联网, 边缘学习, 博弈论, 入侵检测

Abstract:

With the commercialization of 5G and the development of 6G, more and more Internet of things (IoT) devices are linked to the novel cyber-physical system (CPS) to support intelligent decision making.However, the highly decentralized and heterogeneous IoT devices face potential threats that may mislead the CPS.Traditional intrusion detection solutions cannot protect the privacy of IoT devices, and they have to deal with the single point of failure, which prevents these solutions from being deploying in IoT scenarios.The edge learning and game theory based intrusion detection for IoT was proposed.Firstly, an edge learning based intrusion detection framework was proposed to detect potential threats in IoT.Moreover, a multi-leader multi-follower game was employed to motivate trusted parameter servers and edge devices to participate in the edge learning process.Experiments and evaluations show the security and effectiveness of the proposed intrusion detection framework.

Key words: Internet of things, edge learning, game theory, intrusion detection

中图分类号:

TN915.08

梁浩然, 伍军, 赵程程, 李建华. 基于博弈优化边缘学习的物联网入侵检测研究[J]. 物联网学报, 2021, 5(2): 37-47.

Haoran LIANG, Jun WU, Chengcheng ZHAO, Jianhua LI. Leveraging edge learning and game theory for intrusion detection in Internet of things[J]. Chinese Journal on Internet of Things, 2021, 5(2): 37-47.

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符号	定义
N	入侵检测服务器数量
M	边缘设备数量
d_{m ,n}	边缘设备m分配给入侵检测服务器n的入侵样本数量
p_m,n	入侵检测服务器n对边缘设备m中入侵样本的定价
f _m	边缘设备m的计算资源
ε_m	边缘设备m的模型训练精度
B_{n_max}	入侵检测服务器n的最高支出
d_{m_max}	边缘设备m能提供的最大入侵样本数据量

基于博弈优化边缘学习的物联网入侵检测研究

Leveraging edge learning and game theory for intrusion detection in Internet of things

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