Integrated Cyber Physical Assessment and Response for Improved Resiliency | SpringerLink
Skip to main content
Springer Nature Link
Log in

Integrated Cyber Physical Assessment and Response for Improved Resiliency

  • Chapter
  • First Online:
The Internet of Things for Smart Urban Ecosystems

Part of the book series: Internet of Things ((ITTCC))

Abstract

Cyber-physical systems (CPS) are control systems that facilitate the integration of physical systems and computer-based algorithms. These systems are commonly used in control system and critical infrastructure for control and monitoring applications. The internet-of-things (IoT) is a subset of CPS in which multiple physical embedded devices and sensors are connected via a distributed network to communicate and transfer data while being driven by computational algorithms for data delivery and decision-making tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 17159
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 21449
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
JPY 21449
Price includes VAT (Japan)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. F. Alam, R. Mehmood, I. Katib, N.N. Albogami, A. Albeshri, Data-fusion and IoT for smart ubiquitous environments: a survey. IEEE Access 5, 9533–9554 (2017)

    Article  Google Scholar 

  2. T. Vollmer, M. Manic, Cyber-physical system security with deceptive virtual hosts for industrial control networks. IEEE Trans. Ind. Inform. 10(2), 1337–1347 (2014)

    Article  Google Scholar 

  3. O. Linda, D. Wijayasekara, M. Manic, C. Rieger, Computational intelligence-based anomaly detection for building energy management systems, in 2012 5th International Symposium on Resilient Control Systems (2012), pp. 77–82

    Google Scholar 

  4. D. Wijayasekara, M. Manic, C. Rieger, Fuzzy linguistic knowledge-based behavior extraction for building energy management systems, in 2013 6th International Symposium on Resilient Control Systems (ISRCS) (2013), pp. 80–85

    Google Scholar 

  5. T. Vollmer, M. Manic, O. Linda, Autonomic intelligent cyber-sensor to support industrial control network awareness. IEEE Trans. Ind. Inform. 10(2), 1647–1658 (2014)

    Article  Google Scholar 

  6. D.E. Denning, An intrusion-detection model. IEEE Trans. Softw. Eng. 13(2), 222–232 (1987)

    Article  Google Scholar 

  7. R. Sommer, V. Paxson, Outside the closed world: on using machine learning for network intrusion detection, in 2010 IEEE Symposium on Security and Privacy (2010), pp. 305–316

    Google Scholar 

  8. N. Ádám, B. Madoš, A. Baláž, T. Pavlik, Artificial neural network-based IDS, in 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) (2017), pp. 000159–000164

    Google Scholar 

  9. N. Sen, R. Sen, M. Chattopadhyay, An effective back propagation neural network architecture for the development of an efficient anomaly-based intrusion detection system, in 2014 International Conference on Computational Intelligence and Communication Networks (2014), pp. 1052–1056

    Google Scholar 

  10. J. Esmaily, R. Moradinezhad, J. Ghasemi, Intrusion detection system based on multi-layer perceptron neural networks and decision tree, in 2015 7th Conference on Information and Knowledge Technology (IKT) (2015), pp. 1–5

    Google Scholar 

  11. Z. Jadidi, V. Muthukkumarasamy, E. Sithirasenan, M. Sheikhan, Flow-based anomaly detection using neural network optimized with GSA algorithm, in 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops (2013), pp. 76–81

    Google Scholar 

  12. N. Mowla, I. Doh, K. Chae, Evolving neural network intrusion detection system for MCPS, in 2017 19th International Conference on Advanced Communication Technology (ICACT) (2017), pp. 183–187

    Google Scholar 

  13. C. Callegari, S. Giordano, M. Pagano, Neural network-based anomaly detection, in 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) (2014), pp. 310–314

    Google Scholar 

  14. A.M. Kosek, Contextual anomaly detection for cyber-physical security in smart grids based on an artificial neural network model, in 2016 Joint Workshop on Cyber-Physical Security and Resilience in Smart Grids (CPSR-SG) (2016), pp. 1–6

    Google Scholar 

  15. M. Ghanbari, W. Kinsner, K. Ferens, Anomaly detection in a smart grid using wavelet transform, variance fractal dimension and an artificial neural network, in 2016 IEEE Electrical Power and Energy Conference (EPEC) (2016), pp. 1–6

    Google Scholar 

  16. V. Ford, A. Siraj, W. Eberle, Smart-grid energy fraud detection using artificial neural networks, in 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) (2014), pp. 1–6

    Google Scholar 

  17. S. Ntalampiras, Detection of integrity attacks in cyber-physical critical infrastructures using ensemble modeling. IEEE Trans. Ind. Inform. 11(1), 104–111 (2015)

    Article  Google Scholar 

  18. D. Wijayasekara, O. Linda, M. Manic, C. Rieger, FN-DFE: fuzzy-neural data-fusion engine for enhanced resilient state-awareness of hybrid energy systems. IEEE Trans. Cybern. 44(11), 2065–2075 (2014)

    Article  Google Scholar 

  19. E. Hatami, N. Vosoughi, H. Salarieh, Design of a fault tolerated intelligent control system for load following operation in a nuclear power plant. Int. J. Electr. Power Energy Syst. 78, 864–872 (2016)

    Article  Google Scholar 

  20. J. Goh, S. Adepu, M. Tan, Z.S. Lee, Anomaly detection in cyber-physical systems using recurrent neural networks, in 2017 IEEE 18th International Symposium on High Assurance Systems Engineering (HASE) (2017), pp. 140–145

    Google Scholar 

  21. C.G. Cordero, S. Hauke, M. Mühlhäuser, M. Fischer, Analyzing flow-based anomaly intrusion detection using replicator neural networks, in 2016 14th Annual Conference on Privacy, Security and Trust (PST) (2016), pp. 317–324

    Google Scholar 

  22. T. Ince, S. Kiranyaz, L. Eren, M. Askar, M. Gabbouj, Real-time motor fault detection by 1-D convolutional neural networks. IEEE Trans. Ind. Electron. 63(11), 7067–7075 (2016)

    Article  Google Scholar 

  23. Y. Zhou, R. Arghandeh, I. Konstantakopoulos, S. Abdullah, A. von Meier, C.J. Spanos, Abnormal event detection with high resolution micro-PMU data, in 2016 Power Systems Computation Conference (PSCC) (2016), pp. 1–7

    Google Scholar 

  24. S. Brahma, R. Kavasseri, H. Cao, N.R. Chaudhuri, T. Alexopoulos, Y. Cui, Real-time identification of dynamic events in power systems using PMU data, and potential applications #8212: models, promises, and challenges. IEEE Trans. Power Deliv. 32(1), 294–301 (2017)

    Article  Google Scholar 

  25. S.Y. Huang, Y.N. Huang, Network traffic anomaly detection based on growing hierarchical SOM, in 2013 43rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) (2013), pp. 1–2

    Google Scholar 

  26. M. Du, S. Ma, Q. He, A SCADA data-based anomaly detection method for wind turbines, in 2016 China International Conference on Electricity Distribution (CICED) (2016), pp. 1–6

    Google Scholar 

  27. M. Biswal, Y. Hao, P. Chen, S. Brahma, H. Cao, P.D. Leon, Signal features for classification of power system disturbances using PMU data, in 2016 Power Systems Computation Conference (PSCC) (2016), pp. 1–7

    Google Scholar 

  28. K. Wen, J. Yang, F. Cheng, C. Li, Z. Wang, H. Yin, Two-stage detection algorithm for RoQ attack based on localized periodicity analysis of traffic anomaly, in 2014 23rd International Conference on Computer Communication and Networks (ICCCN) (2014), pp. 1–6

    Google Scholar 

  29. M. Gu, The algorithm of information system anomaly detection, in 2013 3rd International Conference on Consumer Electronics, Communications and Networks (2013), pp. 653–657

    Google Scholar 

  30. R.G. Kavasseri, Y. Cui, S.M. Brahma, A new approach for event detection based on energy functions, in 2014 IEEE PES General Meeting | Conference Exposition (2014), pp. 1–5

    Google Scholar 

  31. M. Balchanos, D. Mavris, D.W. Brown, G. Georgoulas, G. Vachtsevanos, Incipient failure detection: a particle filtering approach with application to actuator systems, in 2017 13th IEEE International Conference on Control Automation (ICCA) (2017), pp. 64–69

    Google Scholar 

  32. P. Angelov, Anomaly detection based on eccentricity analysis, in 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) (2014), pp. 1–8

    Google Scholar 

  33. S. Zhu, Y.C. Soh, L. Xie, Distributed inference for relay-assisted sensor networks with intermittent measurements over fading channels. IEEE Trans. Signal Process. 64(3), 742–756 (2016)

    Article  MathSciNet  Google Scholar 

  34. H.E. Garcia, S.M. Meerkov, M.T. Ravichandran, Resilient plant monitoring systems: techniques, analysis, design, and performance evaluation. J. Process Control 32, 51–63 (2015)

    Article  Google Scholar 

  35. X. Cao, P. Cheng, J. Chen, Y. Sun, An online optimization approach for control and communication co-design in networked cyber-physical systems. IEEE Trans. Ind. Inform. 9(1), 439–450 (2013)

    Article  Google Scholar 

  36. I. Friedberg, K. McLaughlin, P. Smith, D. Laverty, and S. Sezer, “STPA-SafeSec: Safety and security analysis for cyber-physical systems,” J. Inf. Secur. Appl

    Google Scholar 

  37. Y. Shoukry, P. Nuzzo, A. Puggelli, A.L. Sangiovanni-Vincentelli, S.A. Seshia, P. Tabuada, Secure state estimation for cyber-physical systems under sensor attacks: a satisfiability modulo theory approach. IEEE Trans. Autom. Control PP(99), 1–1 (2017)

    Google Scholar 

  38. H. Fawzi, P. Tabuada, S. Diggavi, Secure estimation and control for cyber-physical systems under adversarial attacks. IEEE Trans. Autom. Control 59(6), 1454–1467 (2014)

    Article  MathSciNet  Google Scholar 

  39. K. Amarasinghe, D. Wijayasekara, M. Manic, Neural network-based downscaling of building energy management system data, in 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE) (2014), pp. 2670–2675

    Google Scholar 

  40. D. Wijayasekara, M. Manic, Data-fusion for increasing temporal resolution of building energy management system data, in IECON 2015—41st Annual Conference of the IEEE Industrial Electronics Society (2015), pp. 004550–004555

    Google Scholar 

  41. M.J. Ren, L.J. Sun, M.Y. Liu, C.F. Cheung, Y.H. Yin, A reconstruction-registration integrated data-fusion method for measurement of multi-scaled complex surfaces. IEEE Trans. Instrum. Meas. 66(3), 414–423 (2017)

    Article  Google Scholar 

  42. T.R. Bennett, N. Gans, R. Jafari, A data-driven synchronization technique for cyber-physical systems, in Proceedings of the Second International Workshop on the Swarm at the Edge of the Cloud, New York, NY, USA (2015), pp. 49–54

    Google Scholar 

  43. J. Rehder, R. Siegwart, P. Furgale, A general approach to spatiotemporal calibration in multi-sensor systems. IEEE Trans. Robot. 32(2), 383–398 (2016)

    Article  Google Scholar 

  44. L. Sorber, M.V. Barel, L.D. Lathauwer, Structured data-fusion. IEEE J. Sel. Top. Signal Process. 9(4), 586–600 (2015)

    Article  Google Scholar 

  45. Y. Li et al., Conflicts to Harmony: a framework for resolving conflicts in heterogeneous data by truth discovery. IEEE Trans. Knowl. Data Eng. 28(8), 1986–1999 (2016)

    Article  Google Scholar 

  46. B. Andò, S. Baglio, C.O. Lombardo, V. Marletta, A multi-sensor data-fusion approach for ADL and fall classification. IEEE Trans. Instrum. Meas. 65(9), 1960–1967 (2016)

    Article  Google Scholar 

  47. Z. Shan, Y. Xia, P. Hou, J. He, Fusing incomplete multi-sensor heterogeneous data to estimate urban traffic. IEEE Multimed. 23(3), 56–63 (2016)

    Article  Google Scholar 

  48. J. Hu, A.V. Vasilakos, Energy big data analytics and security: challenges and opportunities. IEEE Trans. Smart Grid 7(5), 2423–2436 (2016)

    Article  Google Scholar 

  49. J. Wang, J. Xie, R. Zhao, K. Mao, L. Zhang, A new probabilistic kernel factor analysis for multisensory data-fusion: application to tool condition monitoring. IEEE Trans. Instrum. Meas. 65(11), 2527–2537 (2016)

    Article  Google Scholar 

  50. A. Gautam, Y.C. Soh, Stabilizing model predictive control using parameter-dependent dynamic policy for nonlinear systems modeled with neural networks. J. Process Control 36, 11–21 (2015)

    Article  Google Scholar 

  51. G. Bernieri, S. Damiani, F.D. Moro, L. Faramondi, F. Pascucci, F. Tambone, A multiple-criteria decision-making method as support for critical infrastructure protection and intrusion detection system, in IECON 2016—42nd Annual Conference of the IEEE Industrial Electronics Society (2016), pp. 4871–4876

    Google Scholar 

  52. Y. Zhang, M. Qiu, C.W. Tsai, M.M. Hassan, A. Alamri, Health-CPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst. J. 11(1), 88–95 (2017)

    Article  Google Scholar 

  53. E. Pariser, The filter Bubble: What the Internet is Hiding from you (Penguin, UK, 2011)

    Google Scholar 

  54. G.A. Fink, C.L. North, A. Endert, S. Rose, Visualizing cybersecurity: usable workspaces, in 2009 6th International Workshop on Visualization for Cyber Security (2009), pp. 45–56

    Google Scholar 

  55. J.L. Lamothe, J. She, M. Cheung, Cyber-physical directory: a dynamic visualization of social media data, in 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing (2013), pp. 2007–2012

    Google Scholar 

  56. M. Cheung, J. She, S. Park, Analytics-driven visualization on digital directory via screen-smart device interactions. IEEE Trans. Multimed. 18(11), 2303–2314 (2016)

    Article  Google Scholar 

  57. D. Gürdür, J. El-Khoury, T. Seceleanu, L. Lednicki, Making interoperability visible: Data visualization of cyber-physical systems development tool chains. J. Ind. Inf. Integr. 4, 26–34 (2016)

    Google Scholar 

  58. S. Mittelstaedt, D. Spretke, D. Sacha, D.A. Keim, B. Heyder, J. Kopp, Visual analytics for critical infrastructures, in International ETG-Congress 2013; Symposium 1: Security in Critical Infrastructures Today (2013), pp. 1–8

    Google Scholar 

  59. D. Jäckle, F. Fischer, T. Schreck, D.A. Keim, Temporal MDS plots for analysis of multivariate data. IEEE Trans. Vis. Comput. Graph. 22(1), 141–150 (2016)

    Article  Google Scholar 

  60. D. Wijayasekara, O. Linda, M. Manic, CAVE-SOM: immersive visual data mining using 3D self-organizing maps, in The 2011 International Joint Conference on Neural Networks (2011), pp. 2471–2478

    Google Scholar 

  61. H. Miyachi, K. Koyamada, D. Matsuoka, I. Kuroki, Fusion visualization system as an open science foundation, in 2016 19th International Conference on Network-Based Information Systems (NBiS) (2016), pp. 401–404

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Virginia Commonwealth University, Richmond, USA

    P. Sivils, K. Amarasinghe & M. Manic

  2. Idaho National Laboratory, Idaho Falls, USA

    C. Rieger

Authors
  1. P. Sivils
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Rieger
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. Amarasinghe
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Manic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Sivils .

Editor information

Editors and Affiliations

  1. Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), Rende (CS), Italy

    Franco Cicirelli

  2. Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), Rende (CS), Italy

    Antonio Guerrieri

  3. Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), Rende (CS), Italy

    Carlo Mastroianni

  4. Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), Rende (CS), Italy

    Giandomenico Spezzano

  5. Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), Rende (CS), Italy

    Andrea Vinci

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sivils, P., Rieger, C., Amarasinghe, K., Manic, M. (2019). Integrated Cyber Physical Assessment and Response for Improved Resiliency. In: Cicirelli, F., Guerrieri, A., Mastroianni, C., Spezzano, G., Vinci, A. (eds) The Internet of Things for Smart Urban Ecosystems. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-319-96550-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-96550-5_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96549-9

  • Online ISBN: 978-3-319-96550-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation