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

Abstract

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

CLC Number:

TN915.08

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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References 24

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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

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

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