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

Haoran LIANG; Jun WU; Chengcheng ZHAO; Jianhua LI

doi:10.11959/j.issn.2096-3750.2021.00226

您当前的位置：

首页 >

文章列表页 >

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

Topic: Edge Intelligence and Fog Computing in IoT | 更新时间：2024-06-05

- Leveraging edge learning and game theory for intrusion detection in Internet of things
- Chinese Journal on Internet of Things Vol. 5, Issue 2, Pages: 37-47(2021)
- 作者机构：
  
  1. 上海交通大学，上海 200240
  2. 室兰工业大学，日本室兰 050-8585
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2021.00226
  CLC： TN915.08
- Published：30 June 2021，
  
  Published Online：2021-06，
- 稿件说明：
移动端阅览
HAORAN LIANG, JUN WU, CHENGCHENG ZHAO, et al. 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.
DOI：

HAORAN LIANG, JUN WU, CHENGCHENG ZHAO, et al. 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. DOI： 10.11959/j.issn.2096-3750.2021.00226.

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。

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.

关键词

物联网边缘学习博弈论入侵检测

Keywords

Internet of thingsedge learninggame theoryintrusion detection

references

尤肖虎, 尹浩, 邬贺铨 . 6G 与广域物联网[J]. 物联网学报, 2020,4(1): 3-11.

YOU X H, YIN H, WU H Q . On 6G and wide-area IoT[J]. Chinese Journal on Internet of Things, 2020,4(1): 3-11.

刘怀哲, 高林 . 基于移动热点共享的物联网激励机制研究[J]. 物联网学报, 2019,3(1): 20-29.

LIU H Z, GAO L . Research on incentive mechanism for IoT based on mobile hotspot sharing[J]. Chinese Journal on Internet of Things, 2019,3(1): 20-29.

李赞, 廖晓闽, 石嘉 ,等. 面向认知物联网的隐蔽通信智能功率控制[J]. 物联网学报, 2020,4(1): 52-58.

LI Z, LIAO X M, SHI J ,et al. Intelligent power control for covert communication in cognitive Internet of Things[J]. Chinese Journal on Internet of Things, 2020,4(1): 52-58.

SEDJELMACI H, SENOUCI S M, BU-RGHEFF AM A . An efficient and lightweight intrusion detection mechanism for service-oriented vehicular networks[J]. IEEE Internet of Things Journal, 2014,1(6): 570-577.

ALBERS P, CAMP O, PERCHER J M ,et al. Security in ad hoc networks:a general intrusion detection architecture enhancing trust based approaches[C]// Proceedings of the 1st International Workshop on Wireless Information Systems.[S.l.:s.n.], 2002.

CHIEN W C, CHO H H, LAI C F ,et al. Intelligent architecture for mobile HetNet in B5G[J]. IEEE Network, 2019,33(3): 34-41.

ZHANG T, ZHU Q Y . Distributed privacy-preserving collaborative intrusion detection systems for VANETs[J]. IEEE Transactions on Signal and Information Processing Over Networks, 2018,4(1): 148-161.

SONG H M, KIM H R, KIM H K . Intrusion detection system based on the analysis of time intervals of CAN messages for in-vehicle network[C]// Proceedings of 2016 International Conference on Information Networking (ICOIN). Piscataway:IEEE Press, 2016: 63-68.

LOUKAS G, VUONG T, HEARTFIELD R ,et al. Cloud-based cyber-physical intrusion detection for vehicles using deep learning[J]. IEEE Access, 2018(6): 3491-3508.

CHOI W, JOO K, JO H J ,et al. VoltageIDS:low-level communication characteristics for automotive intrusion detection system[J]. IEEE Transactions on Information Forensics and Security, 2018,13(8): 2114-2129.

SEO E, SONG H M, KIM H K . GIDS:GAN based intrusion detection system for in-vehicle network[C]// Proceedings of 2018 16th Annual Conference on Privacy,Security and Trust (PST). Piscataway:IEEE Press, 2018: 1-6.

MOURAD A, TOUT H, WAHAB O A ,et al. Ad hoc vehicular fog enabling cooperative low-latency intrusion detection[J]. IEEE Internet of Things Journal, 2021,8(2): 829-843.

GROZA B, MURVAY P S . Efficient intrusion detection with bloom filtering in controller area networks[J]. IEEE Transactions on Information Forensics and Security, 2019,14(4): 1037-1051.

MEJRI M N, ACHIR N, HAMDI M . A new security games based reaction algorithm against DOS attacks in VANETs[C]// Proceedings of 2016 13th IEEE Annual Consumer Communications ＆ Networking Conference (CCNC). Piscataway:IEEE Press, 2016: 837-840.

MÜTER M, ASAJ N . Entropy-based anomaly detection for in-vehicle networks[C]// Proceedings of 2011 IEEE Intelligent Vehicles Symposium (IV). Piscataway:IEEE Press, 2011: 1110-1115.

LI X H, HU Z Y, XU M F ,et al. Transfer learning based intrusion detection scheme for Internet of vehicles[J]. Information Sciences, 2021,547: 119-135.

DELWAR HOSSAIN M, INOUE H, OCHIAI H ,et al. An effective in-vehicle CAN bus intrusion detection system using CNN deep learning approach[C]// Proceedings of GLOBECOM 2020 - 2020 IEEE Global Communications Conference. Piscataway:IEEE Press, 2020: 1-6.

STERNE D, BALASUBRAMANYAM P, CARMAN D ,et al. A general cooperative intrusion detection architecture for MANETs[C]// Proceedings of 3rd IEEE International Workshop on Information Assurance (IWIA'05). Piscataway:IEEE Press, 2005: 57-70.

KACHIRSKI O, GUHA R . Effective intrusion detection using multiple sensors in wireless ad hoc networks[C]// Proceedings of 36th Annual Hawaii International Conference on System Sciences,2003. Piscataway:IEEE Press, 2003.

ZHAN Y F, LI P, QU Z H ,et al. A learning-based incentive mechanism for federated learning[J]. IEEE Internet of Things Journal, 2020,7(7): 6360-6368.

KANG J W, XIONG Z H, NIYATO D ,et al. Incentive mechanism for reliable federated learning:a joint optimization approach to combining reputation and contract theory[J]. IEEE Internet of Things Journal, 2019,6(6): 10700-10714.

JIAO Y T, WANG P, NIYATO D ,et al. Toward an automated auction framework for wireless federated learning services market[J]. IEEE Transactions on Mobile Computing,4639,PP(99):1

LIN X, LI J H, WU J ,et al. Making knowledge tradable in edge-AI enabled IoT:a consortium blockchain-based efficient and incentive approach[J]. IEEE Transactions on Industrial Informatics, 2019,15(12): 6367-6378.

XIONG Z H, KANG J W, NIYATO D ,et al. Cloud/edge computing service management in blockchain networks:multi-leader multi-follower game-based ADMM for pricing[J]. IEEE Transactions on Services Computing, 2020,13(2): 356-367.

Views

1062

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Reinforcement learning-based channel access mechanism for multi-base station slotted Aloha with cooperative reception

A survey of federated learning for 6G networks

New flexible sensor for the internet of things

An intrusion detection method based on depthwise separable convolution and attention mechanism

Optical wireless communication and internet of things

Related Author

HUANG Yuankang

ZHAN Wen

SUN Xinghua

Guanglei GENG

Bo GAO

Ke XIONG

Pingyi FAN

Yang LU

Related Institution

Sun Yat-sen University

Sun Yat-sen University Shenzhen Campus

School of Computer and Information Technology, Beijing Jiaotong University

Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education, Beijing Jiaotong University

National Research Center for Information Science and Technology, Tsinghua University

⁰