基于物联网技术的智能电网数据安全问题研究进展

doi:10.16180/j.cnki.issn1007-7820.2023.03.012

Abstract

Abstract:

In order to protect the core data in smart grid equipment and users' personal privacy, a number of Internet of things security technologies such as distributed computing and homomorphic encryption have gradually attracted attention. In recent years, the development of internet of things technology has promoted the rapid popularization of intelligent power grid, and the application of smart grid has promoted the renewal of internet of things technology. Through introducing various attack methods faced by smart grid, this study reviews and combs the research background and current situation of smart grid data security. On this basis, the definitions of false data injection attack and personal privacy protection are discussed and analyzed, and the future research direction and ideas of smart grid data security technology are prospected.

Key words: internet of things, smart grid, privacy protection, data security, data mining, abnormal behavior, distributed computing, machine learning

CLC Number:

TN911

YING Jieyao. Research Progress of Smart Grid Data Security Based on Internet of Things Technology[J].Electronic Science and Technology, 2023, 36(3): 76-80.

References 50

[1]	Farhangi H. The path of the smart grid[J]. IEEE Powerand Energy Magazine, 2009, 8(1):18-28.
[2]	Gungor V C, Sahin D, Kocak T, et al. Smart grid technologies:Communication technologies and standards[J]. IEEE Transactions on Industrial Informatics, 2011, 7(4):529-539. doi: 10.1109/TII.2011.2166794
[3]	Fang X, Misra S, Xue G, et al. Smart grid-the new and improved power grid:A survey[J]. IEEE Communications Surveys & Tutorials, 2012, 14(4):944-980.
[4]	Fruhauf K, Reichart K, Saly S, et al. The application of a database system to online security assessment in an EHV-network[C]. Cleveland: IEEE Conference Proceedings Power Industry Computer Applications Conference,1979.
[5]	Moslehi K, Kumar R. A reliability perspective of the smart grid[J]. IEEE Transactions on Smart Grid, 2010, 1(1):57-64. doi: 10.1109/TSG.2010.2046346
[6]	Metke A R, Ekl R L. Security technology for smart grid networks[J]. IEEE Transactions on Smart Grid, 2010, 1(1):99-107. doi: 10.1109/TSG.2010.2046347
[7]	Liu Y, Ning P, Reiter M K. False data injection attacks against state estimation in electric power grids[J]. ACM Transactions on Information and System Security, 2011, 14(1):1-33.
[8]	Liu X, Li Z Y, Liu X D, et al. Masking transmission line outages via false data injection attacks[J]. IEEE Transactions on Information Forensics and Security, 2016, 11(7):1592-1602. doi: 10.1109/TIFS.2016.2542061
[9]	Le X, Mo Y, Sinopoli B. Integrity data attacks in powermarket operations[J]. IEEE Transactions on Smart Grid, 2011, 2(4):659-666. doi: 10.1109/TSG.2011.2161892
[10]	Tan R, Nguyen H H, Foo E Y S, et al. Modeling and mitigating impact of false data injection attacks on automatic generation control[J]. IEEE Transactions on Information Forensics & Security, 2017, 12(7):1609-1624.
[11]	Sridhar S, Govindarasu M. Model-based attack detection and mitigation for automatic generation control[J]. IEEE Transactions on Smart Grid, 2014, 5(2):580-591. doi: 10.1109/TSG.2014.2298195
[12]	Liu S, Mashayekh S, Kundur D, et al. A framework for modeling cyber-physical switching attacks in smart grid[J]. IEEE Transactions on Emerging Topics in Computing, 2013, 1(2):273-285. doi: 10.1109/TETC.6245516
[13]	Zhao Y, Goldsmith A, Poor H V. Fundamental limits of cyber-physical security in smart power grids[C]. Firenze: Proceedings of the Fifty-second IEEE Conference on Decision and Control, 2013.
[14]	Min B, Varadharajan V. Design and analysis of securityattacks against critical smart grid infrastructures[C]. Tianjin:Proceedings of the Nineteenth International Conference on Engineering of Complex Computer Systems, 2014.
[15]	Bi S, Zhang Y J. Graphical methods for defense against false-data injection attacks on power system state estimation[J]. IEEE Transactions on Smart Grid, 2014, 5(3):1216-1227. doi: 10.1109/TSG.5165411
[16]	Liang J, Sankar L, Kosut O. Vulnerability analysis and consequences of false data injection attack on power system state estimation[J]. IEEE Transactions on Power Systems, 2016, 31(5):3864-3872. doi: 10.1109/TPWRS.2015.2504950
[17]	Giani A, Bitar E, Garcia M, et al. Smart grid data integrity attacks[J]. IEEE Transactions on Smart Grid, 2013, 4(3):1244-1253. doi: 10.1109/TSG.2013.2245155
[18]	Liu X, Bao Z, Lu D, et al. Modeling of local false data injection attacks with reduced network information[J]. IEEE Transactions on Smart Grid, 2015, 6(4):1686-1696. doi: 10.1109/TSG.2015.2394358
[19]	Hug G, Giampapa J A. Vulnerability assessment of ACstate estimation with respect to false data injection cyber-attacks[J]. IEEE Transactions on Smart Grid, 2012, 3(3):1362-1370. doi: 10.1109/TSG.2012.2195338
[20]	Yu Z H, Chin W L. Blind false data injection attack using PCA approximation method in smart grid[J]. IEEE Transactions on Smart Grid, 2015, 6(3):1219-1226. doi: 10.1109/TSG.2014.2382714
[21]	Kim J, Tong L. On topology attack of a smart grid:Undetectable attacks and countermeasures[J]. IEEE Journal on Selected Areas in Communications, 2013, 31(7):1294-1305. doi: 10.1109/JSAC.2013.130712
[22]	Li Z, Shahidehpour M, Alabdulwahab A, et al. Bilevel model for analyzing coordinated cyber-physical attacks on power systems[J]. IEEE Transactions on Smart Grid, 2015, 7(5):2260-2272. doi: 10.1109/TSG.2015.2456107
[23]	Kang J W, Joo I Y, Choi D H. False data injection attacks on contingency analysis:Attack strategies and impact assessment[J]. IEEE Access, 2018(6):8841-8851.
[24]	Chaojun G, Jirutitijaroen P, Motani M. Detecting false data injection attacks in AC state estimation[J]. IEEE Transactions on Smart Grid, 2015, 6(5):2476-2483. doi: 10.1109/TSG.2015.2388545
[25]	杨杉, 谭博, 郭静波. 基于双马尔科夫链的新型能源互联网虚假数据注入攻击检测[J]. 电力自动化设备, 2021, 41(2):131-137.
	Yang Shan, Tan Bo, Guo Jingbo. Detection of false data injection attack for new-type energy internet based on double Markov chains[J]. Electric Power Automation Equipment, 2021, 41(2):131-137.
[26]	Liu X, Zhu P, Zhang Y, et al. A collaborative intrusion detection mechanism against false data injection attack in advanced metering infrastructure[J]. IEEE Transactions on Smart Grid, 2015, 6(5):2435-2443. doi: 10.1109/TSG.2015.2418280
[27]	Sedghi H, Jonckheere E. Statistical structure learning toensure data integrity in smart grid[J]. IEEE Transactions on Smart Grid, 2015, 6(4):1924-1933. doi: 10.1109/TSG.2015.2403329
[28]	Wang X, Luo X, Zhang Y, et al. Detection and isolation of false data injection attacks in smart grids via non-linear interval observer[J]. IEEE Internet of Things Journal, 2019, 6(4):6498-6512. doi: 10.1109/JIoT.6488907
[29]	Li B, Ding T, Huang C, et al. Detecting false data injection attacks against power system state estimation with fast go-decomposition approach[J]. IEEE Transactions on Industrial Informatics, 2018, 15(5):2892-2904. doi: 10.1109/TII.9424
[30]	Manandhar K, Cao X, Hu F, et al. Detection of faults and attacks including false data injection attack in smart grid using Kalman filter[J]. IEEE Transactions on Control of Network Systems, 2014, 1(4):370-379. doi: 10.1109/TCNS.2014.2357531
[31]	Liu X, Li Z, Li Z. Optimal protection strategy against false data injection attacks in power systems[J]. IEEE Transactions on Smart Grid, 2016, 8(4):1802-1810. doi: 10.1109/TSG.2015.2508449
[32]	An Y, Liu D. Multivariate gaussian based false data detection against cyber-attacks[J]. IEEE Access, 2019(7):119804-119812.
[33]	Xue D, Jing X, Liu H. Detection of false data injection attacks in smart grid utilizing ELM-based OCON framework[J]. IEEE Access, 2019, 7(31):31762-31773. doi: 10.1109/ACCESS.2019.2902910
[34]	Yang Q, An D, Min R, et al. On optimal PMU placement-based defense against data integrity attacks in smart grid[J]. IEEE Transactions on Information Forensics and Securtiy, 2017, 12(7):1735-1750.
[35]	Vuković O, Dán G. Security of fully distributed power system state estimation:Detection and mitigation of data integrity attacks[J]. IEEE Journal on Selected Areas in Communications, 2014, 32(7):1500-1508. doi: 10.1109/JSAC.2014.2332106
[36]	Liu Y, Guo W, Fan C I, et al. A practical privacy-preser-ving data aggregation (3PDA) scheme for smart grid[J]. IEEE Transactions on Industrial Informatics, 2018, 15(3):1767-1774. doi: 10.1109/TII.2018.2809672
[37]	Ding Y, Wang B, Wang Y, et al. Secure metering data a-ggregation with batch verification in industrial smart grid[J]. IEEE Transactions on Industrial Informatics, 2020, 16(10):6607-6616. doi: 10.1109/TII.9424
[38]	Zhao S, Li F, Li H, et al. Smart and practical privacy-preserving data aggregation for fog-based smart grids[J]. IEEE Transactions on Information Forensics and Security, 2020, 16(3):521-536. doi: 10.1109/TIFS.2020.3014487
[39]	魏文燕. Paillier同态密码在隐私保护中的应用研究[D]. 焦作: 河南理工大学, 2017.
	Wei Wenyan. Research on the application of Paillier homomorphic encryption in privacy protection[D]. Jiaozuo: Henan Polytechnic University, 2017.
[40]	Chim T W, Yiu S M, Li V O K, et al. PRGA:privacy-preserving recording & gateway-assisted authentication of power usage information for smart grid[J]. IEEE Transactions on Dependable and Secure Computing, 2015, 12(1):85-97. doi: 10.1109/TDSC.2014.2313861
[41]	Abdallah A, Shen X S. A lightweight lattice-based homomorphic privacy-preserving data aggregation scheme for smart grid[J]. IEEE Transactions on Smart Grid, 2016, 9(1):396-405. doi: 10.1109/TSG.2016.2553647
[42]	Gope P, Sikdar B. An efficient data aggregation scheme for privacy-friendly dynamic pricing-based billing and demand-response management in smart grids[J]. IEEE Internet of Things Journal, 2018, 5(4):3126-3135. doi: 10.1109/JIOT.2018.2833863
[43]	Jia W, Zhu H, Cao Z, et al. Human-factor-aware privacy-preserving aggregation in smart grid[J]. IEEE Systems Journal, 2013, 8(2):598-607. doi: 10.1109/JSYST.2013.2260937
[44]	Lyu L, Nandakumar K, Rubinstein B, et al. PPFA:privacy preserving fog-enabled aggregation in smart grid[J]. IEEE Transactions on Industrial Informatics, 2018, 14(8):3733-3744. doi: 10.1109/TII.9424
[45]	Bao H, Lu R. A new differentially private data aggregation with fault tolerance for smart grid communications[J]. IEEE Internet of Things Journal, 2015, 2(3):248-258. doi: 10.1109/JIOT.2015.2412552
[46]	Boneh D, Goh E J, Nissim K. Evaluating 2-DNF formulas on ciphertexts[C]. Cambridge: Proceedings of the Theory of Cryptograph Conference, 2005.
[47]	Gong X, Hua Q S, Qian L, et al. Communication-efficient and privacy-preserving data aggregation without trusted authority[C]. Honolulu: IEEE INFOCOM Conference on Computer Communications, 2018.
[48]	Boudia O RM, Senouci S M, Feham M. Elliptic curve-based secure multidimensional aggregation for smart grid communications[J]. IEEE Sensors Journal, 2017, 17(23):7750-7757. doi: 10.1109/JSEN.2017.2720458
[49]	Zuo X, Li L, Peng H, et al. Privacy-preserving multidimensional data aggregation scheme without trusted authority in smart grid[J]. IEEE Systems Journal, 2020, 15(1):395-406. doi: 10.1109/JSYST.2020.2994363
[50]	Ming Y, Zhang X, Shen X. Efficient privacy-preserving multi-dimensional data aggregation scheme in smart grid[J]. IEEE Access, 2019(7):32907-32921.

Research Progress of Smart Grid Data Security Based on Internet of Things Technology

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