Distributed host-based collaborative detection for false data injection attacks in smart grid cyber-physical system

Journal of Parallel and Distributed Computing

Volumn 103, Issue , 2017, Pages 32-41

  Li, Beibei ^a   Lu, Rongxing ^b   Wang, Wei ^a   Choo, Kim Kwang Raymond ^c,d  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b UNIVERSITY OF NEW BRUNSWICK   (Canada)

^c UNIVERSITY OF TEXAS AT SAN ANTONIO   (United States)

^d UNIVERSITY OF SOUTH AUSTRALIA   (Australia)

Author keywords

Adaptive reputation system; Distributed host based collaborative detection; False data injection attack; Smart grid cyber physical system (CPS)

Indexed keywords

ADAPTIVE SYSTEMS; CYBER PHYSICAL SYSTEM; ELECTRIC POWER TRANSMISSION NETWORKS; EMBEDDED SYSTEMS; NETWORK SECURITY; PHASOR MEASUREMENT UNITS;

COLLABORATIVE DETECTION; CYBER-PHYSICAL SYSTEMS (CPS); FALSE DATA INJECTION ATTACKS; INFORMATION NETWORKING; MAJORITY VOTING ALGORITHM; PHASOR MEASUREMENT UNIT (PMUS); REAL TIME MEASUREMENTS; REPUTATION SYSTEMS;

SMART POWER GRIDS;

EID: 85008640878     PISSN: 07437315     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jpdc.2016.12.012     Document Type: Article

References (41)

1. [1] Bao, H., Lu, R., Li, B., Deng, R., BLITHE: Behavior rule based insider threat detection for smart grid. IEEE Internet Things J. 3:2 (2016), 190–205.
2. [2] Baran, M.E., Kelley, A.W., State estimation for real-time monitoring of distribution systems. IEEE Trans. Power Appar. Syst. 9:3 (1994), 1601–1609.
3. [3] R.B. Bobba, K.M. Rogers, Q. Wang, H. Khurana, K. Nahrstedt, T.J. Overbye, Detecting false data injection attacks on DC state estimation, in: Preprints of the First Workshop on Secure Control Systems, CPSWEEK, Vol. 2010, 2010.
4. [4] Bowman, K., Shenton, L., Parameter estimation for the Beta distribution. J. Stat. Comput. Simul. 43:3–4 (1992), 217–228.
5. [5] Brar, Y., Randhawa, J.S., Optimal power flow using power world simulator. Proc. IEEE Electric Power and Energy Conference, (EPEC), 2010, IEEE, 1–6.
6. [6] Castiglione, A., Pizzolante, R., Esposito, C., De Santis, A., Palmieri, F., Castiglione, A., A collaborative clinical analysis service based on theory of evidence, fuzzy linguistic sets and prospect theory and its application to craniofacial disorders in infants. Future Gener. Comput. Syst. 67 (2017), 230–241.
7. [7] Chen, J., Abur, A., Placement of PMUs to enable bad data detection in state estimation. IEEE Trans. Power Syst. 21:4 (2006), 1608–1615.
8. [8] Chen, J., Shi, L., Cheng, P., Zhang, H., Optimal denial-of-service attack scheduling with energy constraint. IEEE Trans. Automat. Control 60:11 (2015), 3023–3028.
9. [9] Choo, K.-K.R., A conceptual interdisciplinary plug-and-play cyber security framework. ICTs and the Millennium Development Goals, 2014, Springer, 81–99.
10. [10] Cutsem, T.V., Ribbens-Pavell, M., Mili, L., Hypothesis testing identification: A new method for bad data analysis in power system state estimation. IEEE Trans. Power Appar. Syst.(11), 1984, 3239–3252.
11. [11] C. Esposito, A. Castiglione, F. Palmieri, M. Ficco, Trust management for distributed heterogeneous systems by using linguistic term sets and hierarchies, aggregation operators and mechanism design, Future Gener. Comput. Syst., in press. http://dx.doi.org/10.1016/j.future.2015.12.004.
12. [12] N. Falliere, L.O. Murchu, E. Chien, W32. Stuxnet dossier, White paper, Symantec Corp., Security Response, 5, 2011, 6.
13. [13] Fang, H., Xu, L., Choo, K.-K.R., Stackelberg game based relay selection for physical layer security and energy efficiency enhancement in cognitive radio networks. Appl. Math. Comput. 296 (2017), 153–167.
14. [14] Gorman, S., Dreazen, Y.J., Cole, A., Insurgents hack US drones. Wall Street J. 17 (2009), 1–4.
15. [15] Handschin, E., Schweppe, F., Kohlas, J., Fiechter, A., Bad data analysis for power system state estimation. IEEE Trans. Power Appar. Syst. 94:2 (1975), 329–337.
16. [16] Iqbal, S., Kiah, M.L.M., Dhaghighi, B., Hussain, M., Khan, S., Khan, M.K., Choo, K.-K.R., On cloud security attacks: A taxonomy and intrusion detection and prevention as a service. J. Netw. Comput. Appl. 74 (2016), 98–120.
17. [17] Jiang, R., Lu, R., Wang, Y., Luo, J., Shen, C., Shen, X.S., Energy-theft detection issues for advanced metering infrastructure in smart grid. Tsinghua Sci. Technol. 19:2 (2014), 105–120.
18. [18] Kotiuga, W.W., Vidyasagar, M., Bad data rejection properties of weughted least absolute value techniques applied to static state estimation. IEEE Trans. Power Appar. Syst.(4), 1982, 844–853.
19. [19] Kumarage, H., Khalil, I., Tari, Z., Zomaya, A., Distributed anomaly detection for industrial wireless sensor networks based on fuzzy data modelling. J. Parallel Distrib. Comput. 73:6 (2013), 790–806.
20. [20] Li, W., Risk evaluation of wide area measurement and control system. Risk Assessment of Power Systems: Models, Methods, and Applications, 2014, John Wiley & Sons, 313–350.
21. [21] Li, X., Liang, X., Lu, R., Shen, X., Lin, X., Zhu, H., Securing smart grid: cyber attacks, countermeasures, and challenges. IEEE Commun. Mag. 50:8 (2012), 38–45.
22. [22] Li, B., Lu, R., Wang, W., Choo, K.-K.R., DDOA: A Dirichlet-based detection scheme for opportunistic attacks in smart grid cyber-physical system. IEEE Trans. Inf. Forensics Secur. 11:11 (2016), 2415–2425.
23. [23] Liu, Y., Ning, P., Reiter, M.K., False data injection attacks against state estimation in electric power grids. ACM Trans. Inf. Syst. Secur. (TISSEC), 14(1), 2011, 13.
24. [24] Merrill, H.M., Schweppe, F.C., Bad data suppression in power system static state estimation. IEEE Trans. Power Appar. Syst.(6), 1971, 2718–2725.
25. [25] Mrmol, F.G., Prez, G.M., TRIP, a trust and reputation infrastructure-based proposal for vehicular ad hoc networks. J. Netw. Comput. Appl. 35:3 (2012), 934–941.
26. [26] Nordman, M.M., Lehtonen, M., Distributed agent-based state estimation for electrical distribution networks. IEEE Trans. Power Appar. Syst. 20:2 (2005), 652–658.
27. [27] Otrok, H., Mohammed, N., Wang, L., Debbabi, M., Bhattacharya, P., A game-theoretic intrusion detection model for mobile ad hoc networks. Comput. Commun. 31:4 (2008), 708–721.
28. [28] Pasqualetti, F., Drfler, F., Bullo, F., Cyber-physical attacks in power networks: Models, fundamental limitations and monitor design. Proc. 50th IEEE Conference on Decision and Control and European Control Conference, 2011, IEEE, 2195–2201.
29. [29] Patel, A., Alhussian, H., Pedersen, J.M., Bounabat, B., Jnior, J.C., Katsikas, S., A nifty collaborative intrusion detection and prevention architecture for smart grid ecosystems. Comput. Secur. 64 (2017), 92–109.
30. [30] Peng, J., Choo, K.-K.R., Ashman, H., User profiling in intrusion detection: A review. J. Netw. Comput. Appl. 72 (2016), 14–27.
31. [31] Premarathne, U.S., Khalil, I., Atiquzzaman, M., Trust based reliable transmissions strategies for smart home energy consumption management in cognitive radio based smart grid. Ad Hoc Networks 41 (2016), 15–29.
32. [32] Qiu, M., Gao, W., Chen, M., Niu, J.-W., Zhang, L., Energy efficient security algorithm for power grid wide area monitoring system. IEEE Trans. Smart Grid 2:4 (2011), 715–723.
33. [33] Qiu, M., Su, H., Chen, M., Ming, Z., Yang, L.T., Balance of security strength and energy for a PMU monitoring system in smart grid. IEEE Commun. Mag. 50:5 (2012), 142–149.
34. [34] Rajasegarar, S., Leckie, C., Palaniswami, M., Hyperspherical cluster based distributed anomaly detection in wireless sensor networks. J. Parallel Distrib. Comput. 74:1 (2014), 1833–1847.
35. [35] Schweppe, F.C., Wildes, J., Rom, D.B., Power system static-state estimation, parts I, II, and III. IEEE Trans. Power Appar. Syst. 89:1 (1970), 120–135.
36. [36] S. Shin, G. Gu, Conficker and beyond: A large-scale empirical study, in: Proc. 26th Annual Computer Security Applications Conference, ACSAC, 2010, pp. 151–160.
37. [37] Srivatsa, M., Xiong, L., Liu, L., Trustguard: countering vulnerabilities in reputation management for decentralized overlay networks. Proc. 14th International Conference on World Wide Web, (WWW), 2005, ACM, 422–431.
38. [38] Y.L. Sun, Z. Han, W. Yu, K.J.R. Liu, A trust evaluation framework in distributed networks: Vulnerability analysis and defense against attacks, in: Proc. IEEE INFOCOM, 2006, pp. 1–13.
39. [39] Xie, M., Han, S., Tian, B., Parvin, S., Anomaly detection in wireless sensor networks: A survey. J. Netw. Comput. Appl. 34:4 (2011), 1302–1325.
40. [40] L. Xie, Y.L. Mo, B. Sinopoli, False data injection attacks in electricity markets, in: Proc. First IEEE International Conference on Smart Grid Communications, SmartGridComm, 2010, pp. 226–231.
41. [41] Zhang, D., Li, S., Zeng, P., Zang, C., Optimal microgrid control and power-flow study with different bidding policies by using powerworld simulator. IEEE Trans. Sustainable Energy 5:1 (2014), 282–292.