2021, Volume 7, Issue 8
Engineering >> 2021, Volume 7, Issue 8 doi: 10.1016/j.eng.2021.06.007
Programmable Adaptive Security Scanning for Networked Microgrids
a Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794, USA
b Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
Next Previous
Abstract
Communication-dependent and software-based distributed energy resources (DERs) are extensively integrated into modern microgrids, providing extensive benefits such as increased distributed controllability, scalability, and observability. However, malicious cyber-attackers can exploit various potential vulnerabilities. In this study, a programmable adaptive security scanning (PASS) approach is presented to protect DER inverters against various power-bot attacks. Specifically, three different types of attacks, namely controller manipulation, replay, and injection attacks, are considered. This approach employs both software-defined networking technique and a novel coordinated detection method capable of enabling programmable and scalable networked microgrids (NMs) in an ultra-resilient, time-saving, and autonomous manner. The coordinated detection method efficiently identifies the location and type of power-bot attacks without disrupting normal NM operations. Extensive simulation results validate the efficacy and practicality of the PASS for securing NMs.
Keywords
Networked microgrids ; Programmable adaptive security scanning ; Coordinated detection ; Software defined networking
Figures
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
References
[ 1 ] Zhang P. Networked microgrids. Cambridge: Cambridge University Press; 2021. link1
[ 2 ] Tang Z, Zhang P, Krawec WO, Jiang Z. Programmable quantum networked microgrids. IEEE Trans Quantum Engineer 2020;1:1–13. link1
[ 3 ] Tang Z, Qin Y, Jiang Z, Krawec WO, Zhang P. Quantum-secure microgrid. IEEE Trans Power Syst 2021;36(2):1250–63. link1
[ 4 ] Lu LY, Liu HJ, Zhu H, Chu CC. Intrusion detection in distributed frequency control of isolated microgrids. IEEE Trans Smart Grid 2019;10(6):6502–15. link1
[ 5 ] Farhady H, Lee H, Nakao A. Software-defined networking: a survey. Comput Netw 2015;81:79–95.
[ 6 ] Tang Z, Zhang P, Krawec WO. A quantum leap in microgrids security: the prospects of quantum-secure microgrid. IEEE Electrific Mag 2021;9(1):66–73. link1
[ 7 ] Hatziargyriou ND, Kleftakis V, Papadimitriou CN, Messinis G. Microgrids in distribution. In: Liu CC, MaArthur S, Lee SJ, editors. Smart grid handbook. Hoboken: John Wiley & Sons, Ltd.; 2016.
[ 8 ] Zhang P, Wang B, Luh PB, Ren L, Qin Y, inventors; University of Connecticut, assignee. Enabling resilient microgrid through ultra-fast programmable network. United States patent US 20170324671. 2017 Apr 28.
[ 9 ] Wang L, Qin Y, Tang Z, Zhang P. Software-defined microgrid control: the genesis of decoupled cyber–physical microgrids. IEEE Open Access J Power Energy 2020;7:173–82. link1
[10] Huang T, Satchidanandan B, Kumar PR, Xie L. An online detection framework for cyber attacks on automatic generation control. IEEE Trans Power Syst 2018;33(6):6816–27. link1
[11] Ravichandran MT. Resilient monitoring and control systems: design, analysis, and performance evaluation [dissertation]. Michigan: University of Michigan; 2015. link1
[12] Li Y, Zhang P, Zhang L, Wang B. Active synchronous detection of deception attacks in microgrid control systems. IEEE Trans Smart Grid 2017;8(1):373–5. link1
[13] Pan K, Teixeira A, Cvetkovic M, Palensky P. Cyber risk analysis of combined data attacks against power system state estimation. IEEE Trans Smart Grid 2019;10(3):3044–56. link1
[14] Skopik F, Smith PD, editors. Smart grid security: innovative solutions for a modernized grid. Burlington: Syngress; 2015.
[15] Kurt MN, Yilmaz Y, Wang X. Real-time detection of hybrid and stealthy cyberattacks in smart grid. IEEE Trans Inform Forensics Security 2019;14 (2):498–513. link1
[16] Manandhar K, Cao X, Hu F, Liu Y. Detection of faults and attacks including false data injection attack in smart grid using Kalman filter. IEEE Trans Cont Network Syst 2014;1(4):370–9. link1
[17] Tang Z, Jiao J, Zhang P, Yue M, Chen C, Yan J. Enabling cyberattack-resilient load forecasting through adversarial machine learning. 2020. arXiv:2001.02289.
[18] Tan S, De D, Song WZ, Yang J, Das SK. Survey of security advances in smart grid: a data driven approach. IEEE Comm Surv Tutor 2017;19(1): 397–422. link1
[19] Musleh AS, Chen G, Dong ZY. A survey on the detection algorithms for false data injection attacks in smart grids. IEEE Trans Smart Grid 2020;11 (3):2218–34. link1
[20] Wang J, Qin Y, Tang Z, Zhang P. Software-defined cyber–energy secure underwater wireless power transfer. IEEE J Emerg Sel Topics Ind Electron 2021;2(1):21–31. link1
[21] He Y, Mendis GJ, Wei J. Real-time detection of false data injection attacks in smart grid: a deep learning-based intelligent mechanism. IEEE Trans Smart Grid 2017;8(5):2505–16. link1
[22] Babahajiani P, Wang L, Liu J, Zhang P. Push-sum-enabled resilient microgrid control. IEEE Trans Smart Grid. In press.
[23] Ren L, Qin Y, Wang B, Zhang P, Luh PB, Jin R. Enabling resilient microgrid through programmable network. IEEE Trans Smart Grid 2017;8 (6):2826–36. link1
[24] Moslemi R, Mesbahi A, Velni JM. A fast, decentralized covariance selectionbased approach to detect cyber attacks in smart grids. IEEE Trans Smart Grid 2018;9(5):4930–41. link1
[25] Wan W, Bragin MA, Yan B, Qin Y, Philhower J, Zhang P, et al. Distributed and asynchronous active fault management for networked microgrids. IEEE Trans Power Syst 2020;35(5):3857–68. link1
[26] Kreutz D, Ramos FMV, Esteves Verissimo P, Esteve Rothenberg C, Azodolmolky S, Uhlig S. Software-defined networking: a comprehensive survey. Proc IEEE 2015;103(1):14–76. link1
[27] Akilli M, Yilmaz N. Study of weak periodic signals in the EEG signals and their relationship with postsynaptic potentials. IEEE Trans Neural Syst Rehabil Eng 2018;26(10):1918–25. link1
[28] Vahedi H, Gharehpetian GB, Karrari M. Application of duffing oscillators for passive islanding detection of inverter-based distributed generation units. IEEE Trans Power Deliv 2012;27(4):1973–83. link1
[29] Jalilvand A, Fotoohabadi H. The application of Duffing oscillator in weak signal detection. ECTI Trans Electr Engineer Electron Commun 2011;9 (1):1–6. link1
[30] Fujita T. Introduction to Ryu SDN framework [Internet]. Tokyo: Ryu SDN Framework Community; 2013 Apr 15 [cited 2020 Jul 20]; Available from: https://ryu-sdn.org/slides/ONS2013-april-ryu-intro.pdf. link1
[31] Wang MH, Chi PW, Guo JW, Lei CL. SDN storage: a stream-based storage system over software-defined networks, In: Proceedings of 2016 IEEE Conference on Computer Communications Workshops; 2016 Apr 10–14; San Francisco, CA, USA; New York: IEEE; 2016. p. 598–9.
[32] Chithaluru P, Prakash R. Simulation on SDN and NFV models through mininet. In: Damka A, editor. Innovations in software-defined networking and network functions virtualization. Hershey: IGI Global; 2018. p. 149–74. link1
京公网安备 11010502051620号
