Context-awareness using anomaly-based detectors for smart grid domains

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8924, Issue , 2015, Pages 17-34

  Alcaraz, Cristina ^a   Cazorla, Lorena ^a   Fernandez, Gerardo ^a  

^a UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

Context awareness; Control systems; Prevention; Smart Grid

Indexed keywords

CONTROL SYSTEMS; INTERNET; SMART POWER GRIDS;

ANOMALY BASED DETECTION; APPLICATION CONTEXTS; CONTEXT- AWARENESS; CONTROL REQUIREMENTS; INTERDEPENDENT SYSTEMS; PREVENTION; SCIENTIFIC COMMUNITY; SMART GRID;

ELECTRIC POWER TRANSMISSION NETWORKS;

EID: 84942513354     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-17127-2_2     Document Type: Conference Paper

References (31)

1. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15–58 (2009). 15
2. 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)
3. Gyanchandani, M., Rana, J., Yadav, R.: Taxonomy of anomaly based intrusion detection system: a review. Neural Netw. 2(43), 1–14 (2012)
4. 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)
5. 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)
6. Abowd, G.D., Dey, A.K.: Towards a better understanding of context and contextawareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, p. 304. Springer, Heidelberg (1999)
7. Alcaraz, C., Lopez, J.: Wide-area situational awareness for critical infrastructure protection. IEEE Comput. 46(4), 30–37 (2013). IEEE Computer Society
8. Bhuyan, M., Bhattacharyya, D., Kalita, J.: Network anomaly detection: methods, systems and tools. IEEE Commun. Surv. Tutor. 99, 1–34 (2013)
9. Fan, J.: Nonlinear Time Series: Non-parametric And Parametric Methods. Springer, Handbook (2003)
10. Demand Planning, Exponential Smoothing (SCM-APO-FCS), SAP. http://help.sap.com/. Accessed May 2014
11. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2–3), 131–163 (1997)
12. Jyothsna, V., Prasad, R.V.V.: A review of anomaly based intrusion detection systems. Int. J. Comput. Appl. 28(7), 26–35 (2011)
13. 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)
14. Mackay, D.: Information Theory, Inference and Learning Algorithms. Cambridge University Press, Cambridge (2003)
15. 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)
16. 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)
17. 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)
18. 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)
19. Jokar, P.: Model-based intrusion detection for Home Area Networks in Smart Grids, pp. 1–19. University of Bristol, Bristol (2012)
20. 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)
21. Shahid, N., Aleem, S., Naqvi, I., Zaffar, N.: Support vector machine based fault detection & classification in smart grids, pp. 1526–1531. Globecom, IEEE (2012)
22. 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)
23. 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)
24. Sedghi, H., Jonckheere, E.: Statistical structure learning: towards a tobust Smart Grid, arXiv, pp. 1–16 (2014)
25. 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)
26. 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)
27. 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)
28. 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)
29. 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)
30. Chien, C., Chen, S., Lin, Y.: Using bayesian network for fault location on distribution feeder. IEEE Trans. Power Del. 17(3), 785–793 (2002)
31. 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)