Table of Contents Author Guidelines Submit a Manuscript
Security and Communication Networks
Volume 2017 (2017), Article ID 7892182, 19 pages
https://doi.org/10.1155/2017/7892182
Research Article

Predictive Abuse Detection for a PLC Smart Lighting Network Based on Automatically Created Models of Exponential Smoothing

Institute of Telecommunications and Computer Science, Faculty of Telecommunications, Computer Science and Electrical Engineering, University of Technology and Life Sciences in Bydgoszcz (UTP), Ul. Kaliskiego 7, 85-789 Bydgoszcz, Poland

Correspondence should be addressed to Tomasz Andrysiak; lp.ude.ptu@syrdna

Received 23 July 2017; Accepted 19 September 2017; Published 25 October 2017

Academic Editor: Steffen Wendzel

Copyright © 2017 Tomasz Andrysiak et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. IEEE Standards Association, IEEE Guide for Smart Grid Interoperability of Energy Technology and Information Technology Operation with the Electric Power System (EPS), End-Use Applications, and Loads, The Institute of Electrical and Electronics Engineers, 2011.
  2. M. Górczewska, S. Mroczkowska, and P. Skrzypczak, “Badanie wplywu barwy swiatla w oswietleniu drogowym na rozpoznawalnosc przeszkód (light color influence on obstacle recognition,” Electrical Engineering, vol. 73, pp. 165–172, 2013. View at Google Scholar
  3. H. Schaffers, “Landscape and Roadmap of Future Internet and Smart Cities,” 2012.
  4. S. Sun, B. Rong, and Y. Qian, “Artificial frequency selective channel for covert cyclic delay diversity orthogonal frequency division multiplexing transmission,” Security and Communication Networks, vol. 8, no. 9, pp. 1707–1716, 2015. View at Publisher · View at Google Scholar
  5. IEC 62386-102:2014, Digital addressable lighting interface - Part 102: General requirements - Control gear, 2014.
  6. EN 50065-1:2011, Signalling on low-voltage electrical installations in the frequency range 3 kHz to 148.5 kHz, General requirements, frequency bands and electromagnetic disturbances, 2011.
  7. M. A. Faisal, Z. Aung, J. R. Williams, and A. Sanchez, “Data-stream-based intrusion detection system for advanced metering infrastructure in smart grid: a feasibility study,” IEEE Systems Journal, vol. 9, no. 1, pp. 31–44, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. P. Kiedrowski, B. Dubalski, T. Marciniak, T. Riaz, and J. Gutierrez, “Energy greedy protocol suite for smart grid communication systems based on short range devices,” in Image Processing and Communications Challenges 3, vol. 102 of Advances in Intelligent and Soft Computing, pp. 493–502, Springer, Berlin, Germany, 2011. View at Publisher · View at Google Scholar
  9. P. Kiedrowski, “Errors nature of the narrowband plc transmission in smart lighting LV network,” International Journal of Distributed Sensor Networks, vol. 2016, Article ID 9592679, 9 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  10. A. S. Elmaghraby and M. M. Losavio, “Cyber security challenges in smart cities: Safety, security and privacy,” Journal of Advanced Research, vol. 5, no. 4, pp. 491–497, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. M. H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita, “Network anomaly detection: methods, systems and tools,” IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 303–336, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Esposito, C. Mazzariello, F. Oliviero, S. P. Romano, and C. Sansone, “Evaluating pattern recognition techniques in intrusion detection systems,” in Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems (PRIS'05), in Conjunction with ICEIS 2005, pp. 144–153, Miami, FL, USA, May 2005. View at Scopus
  13. R. Mitchell and I.-R. Chen, “Behavior-rule based intrusion detection systems for safety critical smart grid applications,” IEEE Transactions on Smart Grid, vol. 4, no. 3, pp. 1254–1263, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: a survey,” ACM Computing Surveys, vol. 41, no. 3, article 15, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. T. Andrysiak and Ł. Saganowski, Network Anomaly Detection Basedon ARFIMA Model, Image Processing & Communications Challenges 6, Advances in Intelligent Systems and Computing, vol. 313, Springer, 2015.
  16. E. H. M. Pena, M. V. O. De Assis, and M. L. Proença, “Anomaly detection using forecasting methods ARIMA and HWDS,” in Proceedings of the 32nd International Conference of the Chilean Computer Science Society, SCCC 2013, pp. 63–66, November 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. G. Galvas, “Time series forecasting used for real-time anomaly detection on websites,” 2016, https://beta.vu.nl/nl/Images/stageverslag-galvas_tcm235-801861.pdf.
  18. M. Xie, S. Han, B. Tian, and S. Parvin, “Anomaly detection in wireless sensor networks: a survey,” Journal of Network and Computer Applications, vol. 34, no. 4, pp. 1302–1325, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. P. Cheng and M. Zhu, “Lightweight anomaly detection for wireless sensor networks,” International Journal of Distributed Sensor Networks, vol. 2015, Article ID 653232, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. K. Ord and S. Lowe, “Automatic forecasting,” The American Statistician, vol. 50, no. 1, pp. 88–94, 1996. View at Google Scholar · View at Scopus
  21. V. Garcia-Font, C. Garrigues, and H. Rifà-Pous, “A comparative study of anomaly detection techniques for smart city wireless sensor networks,” Sensors, vol. 16, no. 6, article 868, 2016. View at Publisher · View at Google Scholar · View at Scopus
  22. EFT/Burst generator Teseq, http://www.teseq.com/products/NSG-3060.php.
  23. N. Zhou, J. Wang, and Q. Wang, “A novel estimation method of metering errors of electric energy based on membership cloud and dynamic time warping,” IEEE Transactions on Smart Grid, vol. 8, no. 3, pp. 1318–1329, 2017. View at Publisher · View at Google Scholar
  24. V. J. Hodge and J. Austin, “A survey of outlier detection methodologies,” Artificial Intelligence Review, vol. 22, no. 2, pp. 85–126, 2004. View at Publisher · View at Google Scholar · View at Scopus
  25. Y. Wang, T. T. Gamage, and C. H. Hauser, “Security Implications of Transport Layer Protocols in Power Grid Synchrophasor Data Communication,” IEEE Transactions on Smart Grid, vol. 7, no. 2, pp. 807–816, 2016. View at Publisher · View at Google Scholar · View at Scopus
  26. M. Mahoor, F. R. Salmasi, and T. A. Najafabadi, “A hierarchical smart street lighting system with brute-force energy optimization,” IEEE Sensors Journal, vol. 17, no. 9, pp. 2871–2879, 2017. View at Publisher · View at Google Scholar
  27. C. Liao, C.-W. Ten, and S. Hu, “Strategic FRTU deployment considering cybersecurity in secondary distribution network,” IEEE Transactions on Smart Grid, vol. 4, no. 3, pp. 1264–1274, 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. S. M. Rinaldi, J. P. Peerenboom, and T. K. Kelly, “Identifying, understanding, and analyzing critical infrastructure interdependencies,” IEEE Control Systems Magazine, vol. 21, no. 6, pp. 11–25, 2001. View at Publisher · View at Google Scholar · View at Scopus
  29. Y. Wu, C. Shi, X. Zhang, and W. Yang, “Design of new intelligent street light control system,” in Proceedings of the 2010 8th IEEE International Conference on Control and Automation, ICCA 2010, pp. 1423–1427, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  30. T. Macaulay and B. L. Singer, ICS vulnerabilities. In: Cybersecurity industrial control systems SCADA, DCS, PLC, HMI, SIS [Internet], CRC PRESS: Taylor & Francis Group, 2012, https://www.crcpress.com/Cybersecurity-for-Industrial-Control-Systems-SCADA-DCS-PLC-HMI-and/Macaulay-Singer/9781439801963 [Google Scholar].
  31. R. Smoleński, Conducted Electromagnetic Interference (EMI) in Smart Grids, Springer, London, UK, 2012. View at Publisher · View at Google Scholar
  32. J. Liu, Y. Xiao, S. Li, W. Liang, and C. L. P. Chen, “Cyber security and privacy issues in smart grids,” IEEE Communications Surveys & Tutorials, vol. 14, no. 4, pp. 981–997, 2012. View at Publisher · View at Google Scholar · View at Scopus
  33. D. M. Hawkins, Identification of Outliers, Chapman and Hall, London, UK, 1980. View at MathSciNet
  34. M. J. Healy, “Multivariate Normal Plotting,” Journal of Applied Statistics, vol. 17, no. 2, p. 157, 1968. View at Publisher · View at Google Scholar
  35. P. J. Rousseeuw, “Least median of squares regression,” Journal of the American Statistical Association, vol. 79, no. 388, pp. 871–880, 1984. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  36. P. Filzmoser, R. Maronna, and M. Werner, “Outlier identification in high dimensions,” Computational Statistics & Data Analysis, vol. 52, no. 3, pp. 1694–1711, 2008. View at Publisher · View at Google Scholar · View at Scopus
  37. R. L. Goodrich, “The Forecast Pro methodology,” International Journal of Forecasting, vol. 16, no. 4, pp. 533–535, 2000. View at Publisher · View at Google Scholar
  38. R. J. Hyndman, A. B. Koehler, R. D. Snyder, and S. Grose, “A state space framework for automatic forecasting using exponential smoothing methods,” International Journal of Forecasting, vol. 18, no. 3, pp. 439–454, 2002. View at Publisher · View at Google Scholar · View at Scopus
  39. E. S. Gardner, “Exponential smoothing: the state of the art,” Journal of Forecasting, vol. 4, no. 1, pp. 1–28, 1985. View at Publisher · View at Google Scholar · View at Scopus
  40. E. S. Gardner Jr., “Exponential smoothing: the state of the art-part II,” International Journal of Forecasting, vol. 22, no. 4, pp. 637–666, 2006. View at Publisher · View at Google Scholar · View at Scopus
  41. B. C. Archibald, “Parameter space of the holt-winters' model,” International Journal of Forecasting, vol. 6, no. 2, pp. 199–209, 1990. View at Publisher · View at Google Scholar · View at Scopus
  42. J. Durbin and S. J. Koopman, Time series analysis by state space methods, vol. 24, Oxford University Press, Oxford, UK, 2001. View at MathSciNet
  43. R. J. Hyndman and Y. Khandakar, “Automatic time series forecasting: the forecast package for R,” Journal of Statistical Software , vol. 27, no. 3, pp. 1–22, 2008. View at Google Scholar · View at Scopus
  44. H. Bozdogan, “Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions,” Psychometrika, vol. 52, no. 3, pp. 345–370, 1987. View at Publisher · View at Google Scholar · View at MathSciNet
  45. J. Ramsey and D. Wiley, “Book Reviews : exploratory data analysis John W. Tukey Reading, Mass: Addison-Wesley, 1977, Pps. xvi +688. $17.95,” Applied Psychological Measurement, vol. 2, no. 1, pp. 151–155, 1978. View at Publisher · View at Google Scholar
  46. National Fund for Environmental Protection and Water Management under the realized GEKON program (project no. 214093).
  47. IEC 61000-4-4, http://www.iec.ch/emc/basic_emc/basic_emc_immunity.htm.
  48. S. McLaughlin, B. Holbert, A. Fawaz, R. Berthier, and S. Zonouz, “A multi-sensor energy theft detection framework for advanced metering infrastructures,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 7, pp. 1319–1330, 2013. View at Publisher · View at Google Scholar · View at Scopus
  49. Y. Liu, S. Hu, and T.-Y. Ho, “Leveraging strategic detection techniques for smart home pricing cyberattacks,” IEEE Transactions on Dependable and Secure Computing, vol. 13, no. 2, pp. 220–235, 2016. View at Publisher · View at Google Scholar · View at Scopus
  50. C.-H. Lo and N. Ansari, “CONSUMER: a novel hybrid intrusion detection system for distribution networks in smart grid,” IEEE Transactions on Emerging Topics in Computing, vol. 1, no. 1, pp. 33–44, 2013. View at Publisher · View at Google Scholar · View at Scopus