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2018 | OriginalPaper | Buchkapitel

A Computational Intelligence System Identifying Cyber-Attacks on Smart Energy Grids

verfasst von : Konstantinos Demertzis, Lazaros Iliadis

Erschienen in: Modern Discrete Mathematics and Analysis

Verlag: Springer International Publishing

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Abstract

According to the latest projections of the International Energy Agency, smart grid technologies have become essential to handling the radical changes expected in international energy portfolios through 2030. A smart grid is an energy transmission and distribution network enhanced through digital control, monitoring, and telecommunication capabilities. It provides a real-time, two-way flow of energy and information to all stakeholders in the electricity chain, from the generation plant to the commercial, industrial, and residential end user. New digital equipment and devices can be strategically deployed to complement existing equipment. Using a combination of centralized IT and distributed intelligence within critical system control nodes ranging from thermal and renewable plant controls to grid and distribution utility servers to cities, commercial and industrial infrastructures, and homes a smart grid can bring unprecedented efficiency and stability to the energy system. Information and communication infrastructures will play an important role in connecting and optimizing the available grid layers. Grid operation depends on control systems called Supervisory Control and Data Acquisition (SCADA) that monitor and control the physical infrastructure. At the heart of these SCADA systems are specialized computers known as Programmable Logic Controllers (PLCs). There are destructive cyber-attacks against SCADA systems as Advanced Persistent Threats (APT) were able to take over the PLCs controlling the centrifuges, reprogramming them in order to speed up the centrifuges, leading to the destruction of many and yet displaying a normal operating speed in order to trick the centrifuge operators and finally can not only shut things down but can alter their function and permanently damage industrial equipment. This paper proposes a computational intelligence System for Identification Cyber-Attacks on the Smart Energy Grids (SICASEG). It is a big data forensics tool which can capture, record, and analyze the smart energy grid network events to find the source of an attack to both prevent future attacks and perhaps for prosecution.

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Literatur
1.
Zurück zum Zitat Blumsack, S., Fernandez, A.: Ready or not, here comes the smart grid!. Energy 37, 61–68 (2012)CrossRef Blumsack, S., Fernandez, A.: Ready or not, here comes the smart grid!. Energy 37, 61–68 (2012)CrossRef
2.
Zurück zum Zitat Coll-Mayora, D., Pagetb, M., Lightnerc, E.: Future intelligent power grids: analysis of the vision in the European Union and the United States. Energy Policy 35, 2453–2465 (2007)CrossRef Coll-Mayora, D., Pagetb, M., Lightnerc, E.: Future intelligent power grids: analysis of the vision in the European Union and the United States. Energy Policy 35, 2453–2465 (2007)CrossRef
3.
Zurück zum Zitat Gellings C.W.: The Smart Grid: Enabling Energy Efficiency and Demand Response. The Fairmont Press, Lilburn (2009) Gellings C.W.: The Smart Grid: Enabling Energy Efficiency and Demand Response. The Fairmont Press, Lilburn (2009)
4.
Zurück zum Zitat Rohjans, S., Uslar, M., Bleiker, R., Gonzalez, J., Specht, M., Suding, T., Weidelt, T.: Survey of smart grid standardization studies and recommendations. In: First IEEE International Conference on Smart Grid Communications (2010). Print ISBN: 978-1-4244-6510-1 Rohjans, S., Uslar, M., Bleiker, R., Gonzalez, J., Specht, M., Suding, T., Weidelt, T.: Survey of smart grid standardization studies and recommendations. In: First IEEE International Conference on Smart Grid Communications (2010). Print ISBN: 978-1-4244-6510-1
6.
Zurück zum Zitat Wang, W., Tolk, A.: The levels of conceptual interoperability model: applying systems engineering principles to M&S. In: Proceeding, SpringSim ’09 Proceedings of the 2009 Spring Simulation Multiconference, Article No. 168. Society for Computer Simulation International, San Diego (2009) Wang, W., Tolk, A.: The levels of conceptual interoperability model: applying systems engineering principles to M&S. In: Proceeding, SpringSim ’09 Proceedings of the 2009 Spring Simulation Multiconference, Article No. 168. Society for Computer Simulation International, San Diego (2009)
7.
Zurück zum Zitat Widergren, S., Levinson, A., Mater, J., Drummond, R.: Smart grid interoperability maturity model. In: Power & Energy Society General Meeting, IEEE, New York (2010). E-ISBN: 978-1-4244-8357-0 Widergren, S., Levinson, A., Mater, J., Drummond, R.: Smart grid interoperability maturity model. In: Power & Energy Society General Meeting, IEEE, New York (2010). E-ISBN: 978-1-4244-8357-0
8.
Zurück zum Zitat Naruchitparames, J., Gunes, M.H., Evrenosoglu, C.Y.: Secure Communications in the Smart Grid. IEEE, New York (2012) Naruchitparames, J., Gunes, M.H., Evrenosoglu, C.Y.: Secure Communications in the Smart Grid. IEEE, New York (2012)
9.
Zurück zum Zitat Massoud, S.A., Giacomoni, A.M.: Smart grid—safe, secure, self-healing. IEEE Power Energy Mag.—Keeping the Smart Grid Safe 10(1), 33–40 (2012) Massoud, S.A., Giacomoni, A.M.: Smart grid—safe, secure, self-healing. IEEE Power Energy Mag.—Keeping the Smart Grid Safe 10(1), 33–40 (2012)
10.
Zurück zum Zitat Liu, C.-C., Stefanov, A., Hong, J., Panciatici, P.: Intruders in the grid. IEEE Power Energy Mag.—Keeping the Smart Grid Safe 10(1), 58–66 (2012)CrossRef Liu, C.-C., Stefanov, A., Hong, J., Panciatici, P.: Intruders in the grid. IEEE Power Energy Mag.—Keeping the Smart Grid Safe 10(1), 58–66 (2012)CrossRef
11.
Zurück zum Zitat Wei, D., Jafari, Y.L.M., Skare, P.M., Rohde, K.: Protecting smart grid automation systems against cyberattacks. IEEE Trans. Smart Grid 2(4), 782–795 (2011)CrossRef Wei, D., Jafari, Y.L.M., Skare, P.M., Rohde, K.: Protecting smart grid automation systems against cyberattacks. IEEE Trans. Smart Grid 2(4), 782–795 (2011)CrossRef
12.
Zurück zum Zitat Hahn A., Govindarasu, M.: Cyber attack exposure evaluation framework for the smart grid. IEEE Trans. Smart Grid 2(4), 835–843 (2011)CrossRef Hahn A., Govindarasu, M.: Cyber attack exposure evaluation framework for the smart grid. IEEE Trans. Smart Grid 2(4), 835–843 (2011)CrossRef
13.
16.
Zurück zum Zitat Demertzis, K., Iliadis, L.: A hybrid network anomaly and intrusion detection approach based on evolving spiking neural network classification. In: E-Democracy, Security, Privacy and Trust in a Digital World. Communications in Computer and Information Science, vol. 441, pp. 11–23. Springer, Berlin (2014). https://doi.org/10.1007/978-3-319-11710-2_2 Demertzis, K., Iliadis, L.: A hybrid network anomaly and intrusion detection approach based on evolving spiking neural network classification. In: E-Democracy, Security, Privacy and Trust in a Digital World. Communications in Computer and Information Science, vol. 441, pp. 11–23. Springer, Berlin (2014). https://​doi.​org/​10.​1007/​978-3-319-11710-2_​2
17.
Zurück zum Zitat Demertzis, K., Iliadis, L.: Evolving computational intelligence system for malware detection. In: Advanced Information Systems Engineering Workshops. Lecture Notes in Business Information Processing, vol. 178, pp. 322–334. Springer, Berlin (2014). https://doi.org/10.1007/978-3-319-07869-4_30 Demertzis, K., Iliadis, L.: Evolving computational intelligence system for malware detection. In: Advanced Information Systems Engineering Workshops. Lecture Notes in Business Information Processing, vol. 178, pp. 322–334. Springer, Berlin (2014). https://​doi.​org/​10.​1007/​978-3-319-07869-4_​30
18.
19.
Zurück zum Zitat Demertzis, K., Iliadis, L.: Bio-inspired hybrid intelligent method for detecting android malware. In: Proceedings of the 9th KICSS 2014, Knowledge Information and Creative Support Systems, Cyprus, pp. 231–243 (2014). ISBN: 978-9963-700-84-4 Demertzis, K., Iliadis, L.: Bio-inspired hybrid intelligent method for detecting android malware. In: Proceedings of the 9th KICSS 2014, Knowledge Information and Creative Support Systems, Cyprus, pp. 231–243 (2014). ISBN: 978-9963-700-84-4
20.
Zurück zum Zitat Demertzis, K., Iliadis, L.: Evolving smart URL filter in a zone-based policy firewall for detecting algorithmically generated malicious domains. In: Proceedings SLDS (Statistical Learning and Data Sciences) Conference. Lecture Notes in Artificial Intelligence, vol. 9047, pp. 223–233 (Springer, Royal Holloway University, London, 2015). https://doi.org/10.1007/978-3-319-17091-6_17 CrossRef Demertzis, K., Iliadis, L.: Evolving smart URL filter in a zone-based policy firewall for detecting algorithmically generated malicious domains. In: Proceedings SLDS (Statistical Learning and Data Sciences) Conference. Lecture Notes in Artificial Intelligence, vol. 9047, pp. 223–233 (Springer, Royal Holloway University, London, 2015). https://​doi.​org/​10.​1007/​978-3-319-17091-6_​17 CrossRef
24.
Zurück zum Zitat Demertzis, K., Iliadis, L.: SICASEG: a cyber threat bio-inspired intelligence management system. J. Appl. Math. Bioinform. 6(3), 45–64 (2016). ISSN: 1792-6602 (print). 1792-6939 (online). Scienpress Ltd. (2016) Demertzis, K., Iliadis, L.: SICASEG: a cyber threat bio-inspired intelligence management system. J. Appl. Math. Bioinform. 6(3), 45–64 (2016). ISSN: 1792-6602 (print). 1792-6939 (online). Scienpress Ltd. (2016)
27.
Zurück zum Zitat Anezakis, V.D., Demertzis, K., Iliadis, L., Spartalis, S.: A hybrid soft computing approach producing robust forest fire risk indices. In: IFIP Advances in Information and Communication Technology, AIAI September 2016, Thessaloniki, vol. 475, pp. 191–203 (2016) Anezakis, V.D., Demertzis, K., Iliadis, L., Spartalis, S.: A hybrid soft computing approach producing robust forest fire risk indices. In: IFIP Advances in Information and Communication Technology, AIAI September 2016, Thessaloniki, vol. 475, pp. 191–203 (2016)
28.
Zurück zum Zitat Anezakis, V.D., Dermetzis, K., Iliadis, L., Spartalis, S.: Fuzzy cognitive maps for long-term prognosis of the evolution of atmospheric pollution, based on climate change scenarios: the case of Athens. In: Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, vol. 9875, pp. 175–186. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45243-2_16 CrossRef Anezakis, V.D., Dermetzis, K., Iliadis, L., Spartalis, S.: Fuzzy cognitive maps for long-term prognosis of the evolution of atmospheric pollution, based on climate change scenarios: the case of Athens. In: Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, vol. 9875, pp. 175–186. Springer, Cham (2016). https://​doi.​org/​10.​1007/​978-3-319-45243-2_​16 CrossRef
29.
Zurück zum Zitat Bougoudis, I., Demertzis, K., Iliadis, L., Anezakis, V.D., Papaleonidas. A.: Semi-supervised hybrid modeling of atmospheric pollution in urban centers. Commun. Comput. Inform. Sci. 629, 51–63 (2016) Bougoudis, I., Demertzis, K., Iliadis, L., Anezakis, V.D., Papaleonidas. A.: Semi-supervised hybrid modeling of atmospheric pollution in urban centers. Commun. Comput. Inform. Sci. 629, 51–63 (2016)
30.
Zurück zum Zitat Yasakethu, S.L.P., Jiang, J.: Intrusion detection via machine learning for SCADA System Protection. In: Proceedings of the 1st International Symposium for ICS & SCADA Cyber Security Research 2013. Learning and Development Ltd. (2013) Yasakethu, S.L.P., Jiang, J.: Intrusion detection via machine learning for SCADA System Protection. In: Proceedings of the 1st International Symposium for ICS & SCADA Cyber Security Research 2013. Learning and Development Ltd. (2013)
31.
Zurück zum Zitat Chen, Q., Abdelwahed, S.: A model-based approach to self-protection in computing system. In: Proceeding CAC ’13 Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference, Article No. 16 (2013) Chen, Q., Abdelwahed, S.: A model-based approach to self-protection in computing system. In: Proceeding CAC ’13 Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference, Article No. 16 (2013)
33.
Zurück zum Zitat Qin, Y., Cao, X., Liang, P.: Hu, Q.: Zhang, W.: Research on the analytic factor neuron model based on cloud generator and its application in oil&gas SCADA security defense. In: 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems (CCIS) (2014). https://doi.org/10.1109/CCIS.2014.7175721 Qin, Y., Cao, X., Liang, P.: Hu, Q.: Zhang, W.: Research on the analytic factor neuron model based on cloud generator and its application in oil&gas SCADA security defense. In: 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems (CCIS) (2014). https://​doi.​org/​10.​1109/​CCIS.​2014.​7175721
36.
Zurück zum Zitat Pan, S., Morris, T., Adhikari, U.: A specification-based intrusion detection framework for cyber-physical environment in electric power system. Int. J. Netw. Secur. 17(2), 174–188 (2015) Pan, S., Morris, T., Adhikari, U.: A specification-based intrusion detection framework for cyber-physical environment in electric power system. Int. J. Netw. Secur. 17(2), 174–188 (2015)
37.
Zurück zum Zitat Beaver, J., Borges, R., Buckner, M., Morris, T., Adhikari, U., Pan, S.: Machine learning for power system disturbance and cyber-attack discrimination. In: Proceedings of the 7th International Symposium on Resilient Control Systems, Denver, CO (2014) Beaver, J., Borges, R., Buckner, M., Morris, T., Adhikari, U., Pan, S.: Machine learning for power system disturbance and cyber-attack discrimination. In: Proceedings of the 7th International Symposium on Resilient Control Systems, Denver, CO (2014)
38.
Zurück zum Zitat Cambria, E., Guang-Bin, H.: Extreme learning machines. In: IEEE InTeLLIGenT SYSTemS. 541-1672/13 (2013) Cambria, E., Guang-Bin, H.: Extreme learning machines. In: IEEE InTeLLIGenT SYSTemS. 541-1672/13 (2013)
39.
Zurück zum Zitat Price, K., Storn, M., Lampinen, A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Berlin (2005). ISBN: 978-3-540-20950-8MATH Price, K., Storn, M., Lampinen, A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Berlin (2005). ISBN: 978-3-540-20950-8MATH
40.
Zurück zum Zitat Ho-Huu, V., Nguyen-Thoi, T., Vo-Duy, T., Nguyen-Trang, T.: An adaptive elitist differential evolution for optimization of truss structures with discrete design variables. Comput. Struct. 165, 59–75 (2016)CrossRef Ho-Huu, V., Nguyen-Thoi, T., Vo-Duy, T., Nguyen-Trang, T.: An adaptive elitist differential evolution for optimization of truss structures with discrete design variables. Comput. Struct. 165, 59–75 (2016)CrossRef
41.
Zurück zum Zitat Demertzis, K., Iliadis, L.: Adaptive elitist differential evolution extreme learning machines on big data: intelligent recognition of invasive species. In: International Neural Network Society Conference on Big Data (INNS Big Data 2016), Thessaloniki, Proceedings. Advances in Big Data, pp. 23–25. Advances in Intelligent Systems and Computing, vol. 529, pp. 333–345. Springer, Cham (2016) https://doi.org/10.1007/978-3-319-47898-2_34 Demertzis, K., Iliadis, L.: Adaptive elitist differential evolution extreme learning machines on big data: intelligent recognition of invasive species. In: International Neural Network Society Conference on Big Data (INNS Big Data 2016), Thessaloniki, Proceedings. Advances in Big Data, pp. 23–25. Advances in Intelligent Systems and Computing, vol. 529, pp. 333–345. Springer, Cham (2016) https://​doi.​org/​10.​1007/​978-3-319-47898-2_​34
Metadaten
Titel
A Computational Intelligence System Identifying Cyber-Attacks on Smart Energy Grids
verfasst von
Konstantinos Demertzis
Lazaros Iliadis
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-74325-7_5