Abstract
Anomaly-based detection applied in strongly interdependent systems, like Smart Grids, has become one of the most challenging research areas in recent years. Early detection of anomalies so as to detect and prevent unexpected faults or stealthy threats is attracting a great deal of attention from the scientific community because it offers potential solutions for context-awareness. These solutions can also help explain the conditions leading up to a given situation and help determine the degree of its severity. However, not all the existing approaches within the literature are equally effective in covering the needs of a particular scenario. It is necessary to explore the control requirements of the domains that comprise a Smart Grid, identify, and even select, those approaches according to these requirements and the intrinsic conditions related to the application context, such as technological heterogeneity and complexity. Therefore, this paper analyses the functional features of existing anomaly-based approaches so as to adapt them, according to the aforementioned conditions. The result of this investigation is a guideline for the construction of preventive solutions that will help improve the context-awareness in the control of Smart Grid domains in the near future.
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References
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15–58 (2009). 15
Kotsiantis, S., Zaharakis, I., Pintelas, P.: Supervised machine learning: a review of classification techniques. In: Frontiers in Artificial Intelligence and Applications, pp. 249–268 (2007)
Gyanchandani, M., Rana, J., Yadav, R.: Taxonomy of anomaly based intrusion detection system: a review. Neural Netw. 2(43), 1–14 (2012)
Yan, Y., Qian, Y., Sharif, H., Tipper, D.: A survey on smart grid communication infrastructures: motivations, requirements and challenges. IEEE Commun. Surv. Tutor. 15(1), 5–20 (2013)
Roman, R., Alcaraz, C., Lopez, J.: A survey of cryptographic primitives and implementations for hardware-constrained sensor network nodes. Mob. Netw. Appl. 12(4), 231–244 (2007)
Abowd, G.D., Dey, A.K.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, p. 304. Springer, Heidelberg (1999)
Alcaraz, C., Lopez, J.: Wide-area situational awareness for critical infrastructure protection. IEEE Comput. 46(4), 30–37 (2013). IEEE Computer Society
Bhuyan, M., Bhattacharyya, D., Kalita, J.: Network anomaly detection: methods, systems and tools. IEEE Commun. Surv. Tutor. 99, 1–34 (2013)
Fan, J.: Nonlinear Time Series: Non-parametric And Parametric Methods. Springer, Handbook (2003)
Demand Planning, Exponential Smoothing (SCM-APO-FCS), SAP. http://help.sap.com/. Accessed May 2014
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2–3), 131–163 (1997)
Jyothsna, V., Prasad, R.V.V.: A review of anomaly based intrusion detection systems. Int. J. Comput. Appl. 28(7), 26–35 (2011)
Shanmugam, B., Idris, N.: Hybrid intrusion detection systems (HIDS) using Fuzzy logic. In: Skrobanek, P. (ed.) Intrusion Detection Systems, pp. 135–155, Chap. 8. InTech (2011)
Mackay, D.: Information Theory, Inference and Learning Algorithms. Cambridge University Press, Cambridge (2003)
Cazorla, L., Alcaraz, C., Lopez, J.: Towards automatic critical infrastructure protection through machine learning. In: Luiijf, E., Hartel, P. (eds.) CRITIS 2013. LNCS, vol. 8328, pp. 197–203. Springer, Heidelberg (2013)
Chow, M., Yee, S., Taylor, L.: Recognizing animal-caused faults in power distribution systems using artificial neural networks. IEEE Trans. Power Delivery 8(3), 1268–1274 (1993)
Choi, K., Chen, X., Li, S., Kim, M., Chae, K., Na, J.: Intrusion detection of NSM based DoS attacks using data mining in Smart Grid. Energies 5, 4091–4109 (2012)
Kher, S., Nutt, V., Dasgupta, D., Ali, H., Mixon, P.: A detection model for anomalies in smart grid with sensor network. In: Future Instrumentation International Workshop (FIIW), pp. 1–4 (2012)
Jokar, P.: Model-based intrusion detection for Home Area Networks in Smart Grids, pp. 1–19. University of Bristol, Bristol (2012)
Najy, W., Zeineldin, H., Alaboudy, A., Woon, W.: A bayesian passive islanding detection method for inverter-based distributed generation using ESPRIT. IEEE Trans. Power Delivery 26, 2687–2696 (2011)
Shahid, N., Aleem, S., Naqvi, I., Zaffar, N.: Support vector machine based fault detection & classification in smart grids, pp. 1526–1531. Globecom, IEEE (2012)
Zhang, Y., Wang, L., Sun, W., Green, R., Alam, M.: Distributed intrusion detection system in a multi-layer network architecture of smart grids. IEEE Trans. Smart Grid 2(4), 796–808 (2011)
Mitchell, R., Chen, I.R.: Behavior rule based intrusion detection systems for safety critical smart grid applications. IEEE Trans. Smart Grid 4, 1254–1263 (2013)
Sedghi, H., Jonckheere, E.: Statistical structure learning: towards a tobust Smart Grid, arXiv, pp. 1–16 (2014)
Chan, S., Tsui, K., Wu, H., Hou, Y., Wu, Y., Wu, F.: Load/price forescasting and managing demand response for smart grids. IEEE Signal Process. Mag. 29, 68–85 (2012)
Chang, C., Wang, Z., Yang, F., Tan, W.: Hierarchical fuzzy logic system for implementing maintenance schedules of offshore power systems. IEEE Trans. Smart Grid 3(1), 3–11 (2012)
Manjili, Y., Rajaee, A., Jamshidi, M., Kelley, B.: Fuzzy control of electricity storage unit for energy management of Micro-Grids. In: World Automation Congress, pp. 1–6. IEEE (2012)
Calderaro, V., Piccolo, A., Siano, P.: Failure identification in smart grids based on petri net modeling. IEEE Trans. Industr. Electron. 58(10), 4613–4623 (2011)
Syafaruddin, S., Karatepe, E., Hiyama, T.: Controlling of artificial neural network for fault diagnosis of photovoltaic array. In: The 16th International Conference on Intelligent System Application to Power Systems, pp. 1–6. IEEE (2011)
Chien, C., Chen, S., Lin, Y.: Using bayesian network for fault location on distribution feeder. IEEE Trans. Power Del. 17(3), 785–793 (2002)
Samantaray, S., El-Arroudi, K., Joos, G., Kamwa, I.: A Fuzzy rule-based approach for islanding detection in distributed generation. IEEE Trans. Power Delivery 25(3), 1427–1433 (2010)
Acknowledgment
The results of this research have received funding from the Marie-Curie COFUND programme U-Mobility, co-financed by the University of Málaga, the EC FP7 under GA No. 246550 and the Ministerio de Economía y Competitividad (COFUND2013-40259). The second author has been funded by a FPI fellowship from the Junta de Andalucía through the project FISICCO (P11-TIC-07223). Additionally, this work has been partially supported by the research project ARES (CSD2007-00004) and the EU FP7 project FACIES (HOME/2011/CIPS/AG/4000002115).
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Alcaraz, C., Cazorla, L., Fernandez, G. (2015). Context-Awareness Using Anomaly-Based Detectors for Smart Grid Domains. In: Lopez, J., Ray, I., Crispo, B. (eds) Risks and Security of Internet and Systems. CRiSIS 2014. Lecture Notes in Computer Science(), vol 8924. Springer, Cham. https://doi.org/10.1007/978-3-319-17127-2_2
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DOI: https://doi.org/10.1007/978-3-319-17127-2_2
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