Skip to main content
SpringerLink
Log in

Confidence-aware collaborative detection mechanism for false data attacks in smart grids

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Nowadays, the false data injection attack (FDIA), which can bring inestimable losses to smart grids, has become one of the most threatening cyber attacks in cyber physical systems. Previous studies for false data detection focused on state estimation, which require a huge computational overhead at the control center. In this paper, we propose a confidence-aware collaborative detection mechanism for false data attacks, which is a fast and lightweight scheme. Firstly, we propose a trust-based compromised PMU identification method, in order to identify malicious PMUs by monitoring behaviors of PMUs in a cycle. Secondly, we propose a voting-based detection method based on physical rules, in order to detect FDIA collaboratively. This method improves the detection rate while reducing the computational cost at control center. We also make extensive experiments on real-time data that are collected from the PowerWorld simulator. The experimental results show the efficiency and effectiveness of our proposed mechanism and methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Amin S, Cárdenas AA, Sastry SS (2009) Safe and secure networked control systems under denial-of-service attacks. In: International workshop on hybrid systems: computation and control, pp 31–45. Springer

  • Bao H, Lu R, Li B, Deng R (2015) Blithe: behavior rule-based insider threat detection for smart grid. IEEE Internet Things J 3(2):190–205

    Article  Google Scholar 

  • Brar YS, Randhawa JS, et al. (2010) Optimal power flow using power world simulator. In: 2010 ieee electrical power & energy conference, pp 1–6. IEEE

  • Chaojun G, Jirutitijaroen P, Motani M (2015) Detecting false data injection attacks in ac state estimation. IEEE Trans Smart Grid 6(5):2476–2483

    Article  Google Scholar 

  • Che L, Liu X, Shuai Z, Li Z, Wen Y (2018) Cyber cascades screening considering the impacts of false data injection attacks. IEEE Trans Power Syst 33(6):6545–6556

    Article  Google Scholar 

  • Chen J, Abur A (2006) Placement of pmus to enable bad data detection in state estimation. IEEE Trans Power Syst 21(4):1608–1615

    Article  Google Scholar 

  • Chen P-Y, Yang S, McCann JA, Lin J, Yang X (2015) Detection of false data injection attacks in smart-grid systems. IEEE Commun Mag 53(2):206–213

    Article  Google Scholar 

  • Cosovic M, Vukobratovic D (2016) Distributed Gauss-Newton method for ac state estimation: a belief propagation approach. In: 2016 IEEE international conference on smart grid communications (SmartGridComm), pp 643–649. IEEE

  • Das A, Islam MM (2011) Securedtrust: a dynamic trust computation model for secured communication in multiagent systems. IEEE Trans Dependable Secure Comput 9(2):261–274

    Article  Google Scholar 

  • Deng R, Xiao G, Lu R (2015) Defending against false data injection attacks on power system state estimation. IEEE Trans Ind Inf 13(1):198–207

    Article  Google Scholar 

  • Esmalifalak M, Nguyen H, Zheng R, Han Z (2011) Stealth false data injection using independent component analysis in smart grid. In: 2011 IEEE international conference on smart grid communications (SmartGridComm), pp 244–248. IEEE

  • Esmalifalak M, Liu L, Nguyen N, Zheng R, Han Z (2014) Detecting stealthy false data injection using machine learning in smart grid. IEEE Syst J 11(3):1644–1652

    Article  Google Scholar 

  • He Y, Mendis GJ, Wei J (2017) Real-time detection of false data injection attacks in smart grid: a deep learning-based intelligent mechanism. IEEE Trans Smart Grid 8(5):2505–2516

    Article  Google Scholar 

  • Hug G, Giampapa JA (2012) Vulnerability assessment of ac state estimation with respect to false data injection cyber-attacks. IEEE Trans Smart Grid 3(3):1362–1370

    Article  Google Scholar 

  • Kekatos V, Giannakis GB, Baldick R (2014) Grid topology identification using electricity prices. In: 2014 IEEE PES general meeting| conference & exposition, pp 1–5. IEEE

  • Kurt MN, Yılmaz Y, Wang X (2018) Distributed quickest detection of cyber-attacks in smart grid. IEEE Trans Inf Forensics Secur 13(8):2015–2030

    Article  Google Scholar 

  • Li B, Lu R, Wang W, Choo K-KR (2016a) Ddoa: a dirichlet-based detection scheme for opportunistic attacks in smart grid cyber-physical system. IEEE Trans Inf Forensics Secur 11(11):2415–2425

    Article  Google Scholar 

  • Li B, Lu R, Wei W, Choo KKR (2016b) Distributed host-based collaborative detection for false data injection attacks in smart grid cyber-physical system. J Parallel Distrib Comput 103(May):32–41

    Google Scholar 

  • Liang G, Weller SR, Zhao J, Luo F, Dong ZY (2016a) The 2015 Ukraine blackout: implications for false data injection attacks. IEEE Trans Power Syst 32(4):3317–3318

    Article  Google Scholar 

  • Liang G, Zhao J, Luo F, Weller SR, Dong ZY (2016b) A review of false data injection attacks against modern power systems. IEEE Trans Smart Grid 8(4):1630–1638

    Article  Google Scholar 

  • Liu Y, Ning P, Reiter MK (2011) False data injection attacks against state estimation in electric power grids. ACM Trans Inf Syst Secur (TISSEC) 14(1):13

    Article  Google Scholar 

  • Manandhar K, Cao X, Hu F, Liu Y (2014) Detection of faults and attacks including false data injection attack in smart grid using Kalman filter. IEEE Trans Control Netw Syst 1(4):370–379

    Article  MathSciNet  Google Scholar 

  • Mo Y, Kim TH-J, Brancik K, Dickinson D, Lee H, Perrig A, Sinopoli B (2011) Cyber–physical security of a smart grid infrastructure. Proc IEEE 100(1):195–209

    Google Scholar 

  • Muneeswari B, Manikandan MSK (2019) Defending against false data attacks in 3d grid-based manet using soft computing approaches. Soft Comput 23(18):8579–8595

    Article  Google Scholar 

  • Musleh AS, Khalid HM, Muyeen SM, Al-Durra A (2017) A prediction algorithm to enhance grid resilience toward cyber attacks in wamcs applications. IEEE Syst J 13(1):710–719

    Article  Google Scholar 

  • Musleh AS, Chen G, Dong ZY (2020) A survey on the detection algorithms for false data injection attacks in smart grids. IEEE Trans Smart Grid 11(3):2218–2234

    Article  Google Scholar 

  • Ozay M, Esnaola I, Vural FTY, Kulkarni SR, Poor HV (2015) Machine learning methods for attack detection in the smart grid. IEEE Trans Neural Netw Learn Syst 27(8):1773–1786

    Article  MathSciNet  Google Scholar 

  • Pan K, Teixeira A, Cvetkovic M, Palensky P (2018) Cyber risk analysis of combined data attacks against power system state estimation. IEEE Trans Smart Grid 10(3):3044–3056

    Article  Google Scholar 

  • Pei C, Xiao Y, Liang W, Han X (2020) Pmu placement protection against coordinated false data injection attacks in smart grid. IEEE Trans Ind Appl 56(4):4381–4393

    Google Scholar 

  • Peng X, Peidong Z, Zhenyu Z, Pengshuai C, Yinqiao X (2018) Detectors on edge nodes against false data injection on transmission lines of smart grid. Electronics 7(6):89–91

    Article  Google Scholar 

  • Qiu M, Gao W, Chen M, Niu J-W, Zhang L (2011) Energy efficient security algorithm for power grid wide area monitoring system. IEEE Trans Smart Grid 2(4):715–723

    Article  Google Scholar 

  • Qiu M, Su H, Chen M, Ming Z, Yang LT (2012) Balance of security strength and energy for a pmu monitoring system in smart grid. IEEE Commun Mag 50(5):142–149

    Article  Google Scholar 

  • Rahman MDA, Mohsenian-Rad H (2012) False data injection attacks with incomplete information against smart power grids. In: 2012 IEEE Global communications conference (GLOBECOM), pp 3153–3158. IEEE

  • Sedghi H, Jonckheere E (2015) Statistical structure learning to ensure data integrity in smart grid. IEEE Trans Smart Grid 6(4):1924–1933

    Article  Google Scholar 

  • Tajer A (2017) False data injection attacks in electricity markets by limited adversaries: stochastic robustness. IEEE Trans Smart Grid 10(1):128–138

    Article  MathSciNet  Google Scholar 

  • Valdes A, Macwan R, Backes M (2016) Anomaly detection in electrical substation circuits via unsupervised machine learning. In: 2016 IEEE 17th international conference on information reuse and integration (IRI), pp 500–505. IEEE

  • Wang W, Lu Z (2013) Cyber security in the smart grid: survey and challenges. Comput Netw 57(5):1344–1371

    Article  Google Scholar 

  • Wang X, Luo X, Zhang M, Guan X (2019) Distributed detection and isolation of false data injection attacks in smart grids via nonlinear unknown input observers. Int J Electr Power & Energy Syst 110:208–222

    Article  Google Scholar 

  • Yan Y, Qian Y, Sharif H, Tipper D (2012) A survey on cyber security for smart grid communications. IEEE Commun Surveys & Tutorials 14(4):998–1010

    Article  Google Scholar 

  • Yang Q, Yang J, Yu W, An D, Zhang N, Zhao W (2013) On false data-injection attacks against power system state estimation: modeling and countermeasures. IEEE Trans Parallel Distrib Syst 25(3):717–729

    Article  Google Scholar 

  • Yuan Y, Li Z, Ren K (2011) Modeling load redistribution attacks in power systems. IEEE Trans Smart Grid 2(2):382–390

    Article  Google Scholar 

  • Zhang D, Li S, Zeng P, Zang C (2013) Optimal microgrid control and power-flow study with different bidding policies by using powerworld simulator. IEEE Trans Sustain Energy 5(1):282–292

    Article  Google Scholar 

  • Zhao J, Zhang G, La Scala M, Dong ZY, Chen C, Wang J (2015) Short-term state forecasting-aided method for detection of smart grid general false data injection attacks. IEEE Trans Smart Grid 8(4):1580–1590

    Article  Google Scholar 

  • Zimmerman RD, Murillo-Sánchez CE, Thomas RJ (2010) Matpower: steady-state operations, planning, and analysis tools for power systems research and education. IEEE Trans Power Syst 26(1):12–19

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61902040), the Natural Science Foundation of Hunan Province (Grant No. 2019JJ40314), the National Natural Science Key Foundation of China (No. U1966207), and the Scientific Research Fund of Hunan Provincial Education Department (No. 20B015).

Author information

Authors and Affiliations

  1. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China

    Zhuoqun Xia, Gaohang Long & Bo Yin

Authors
  1. Zhuoqun Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gaohang Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bo Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Yin.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xia, Z., Long, G. & Yin, B. Confidence-aware collaborative detection mechanism for false data attacks in smart grids. Soft Comput 25, 5607–5618 (2021). https://doi.org/10.1007/s00500-020-05557-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-05557-5

Keywords

Search

Navigation