Review on Security of Power Internet of Things Terminals

被引：0
作者：
Su S. [1 ]
Wang G. [1 ]
Liu L. [1 ]
Chen Q. [1 ]
Wang K. [1 ]
机构：
[1] College of Electrical Engineering, Changsha University of Science and Technology, Changsha
来源：
Gaodianya Jishu/High Voltage Engineering | 2022年 / 48卷 / 02期
基金：
湖南省自然科学基金; 中国国家自然科学基金; 国家重点研发计划;
关键词：
Data privacy security; Encrypted authentication; Malicious base station; Power internet of things devices; Security protection; Time synchronization attack;
D O I：
10.13336/j.1003-6520.hve.20210150
中图分类号：
学科分类号：
摘要：
With the advancement of sensing technology and the miniaturization of sensors, the Internet of Things (IoT) technology has been rapidly developed in the power system. Due to the large number, extensive interconnection, and decentralized distribution of the power distribution IoT terminals, they have jumped out of the scope of border security protection, which has caused the terminals to become the main target and springboard for attacking the power grid. Firstly, combined with the power IoT architecture, the terminal security challenges are summarized from the aspects of software, hardware, communication, etc., the terminal risk characteristics are further summarized, and the protection mechanism deployed to deal with the terminal risk is analyzed and its shortcomings are presented. Then, according to the existing protection vul-nerabilities, the terminal security issues are analyzed, the encryption and authentication issues, data privacy security, time synchronization attack, malicious base station attack are focused on, and the communication block attack of 230 MHz power IoT terminal and their corresponding protection research are reviewed. Finally, the applications of new generation AI technologies such as deep learning in the security of IoT terminals are reviewed and prospected. © 2022, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
引用
收藏
页码：513 / 525
页数：12
相关论文
共 76 条
  • [61] CHOUCHANE A, REKHIS S, BOUDRIGA N., Defending against rogue base station attacks using wavelet based fingerprinting, Proceedings of 2009 IEEE/ACS International Conference on Computer Systems and Applications, pp. 523-530, (2009)
  • [62] XU Q, ZHENG R, SAAD W, Et al., Device fingerprinting in wireless networks: challenges and opportunities, IEEE Communications Surveys & Tutorials, 18, 1, pp. 94-104, (2016)
  • [63] POLAK A C, GOECKEL D L., Wireless device identification based on RF oscillator imperfections, IEEE Transactions on Information Forensics and Security, 10, 12, pp. 2492-2501, (2015)
  • [64] LIU Ming, LIU Nian, HAN Xiaoyi, Et al., A RF fingerprint based EIoT device identification method, Electric Power, 54, 3, pp. 80-88, (2021)
  • [65] WANG W, YANG L, ZHANG Q, Et al., Securing on-body IoT devices by exploiting creeping wave propagation, IEEE Journal on Selected Areas in Com-munications, 36, 4, pp. 696-703, (2018)
  • [66] XIAO Yong, QIAN Bin, CAI Ziwen, Et al., Malicious wireless communication link detection of power internet of thing devices, Transactions of China Electrotechnical Society, 35, 11, pp. 2319-2327, (2020)
  • [67] LUO Junzhou, YANG Ming, LING Zhen, Et al., Anonymous communication and darknet: a survey, Journal of Computer Research and Development, 56, 1, pp. 103-130, (2019)
  • [68] KORONIOTIS N, MOUSTAFA N, SITNIKOVA E., Forensics and deep learning mechanisms for botnets in internet of things: a survey of challenges and solutions, IEEE Access, 7, pp. 61764-61785, (2019)
  • [69] LI Yun, HUANG Chenlin, WANG Zhongfeng, Et al., Survey of software vulnerability mining methods based on machine learning, Journal of Software, 31, 7, pp. 2040-2061, (2020)
  • [70] HSU J., DARPA challenge tests AI as cybersecurity defenders, IEEE Spectrum, (2016)
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