Integrated Cyber Physical Assessment and Response for Improved Resiliency

  • P. SivilsEmail author
  • C. Rieger
  • K. Amarasinghe
  • M. Manic
Part of the Internet of Things book series (ITTCC)


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.


  1. 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)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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. 77–82Google Scholar
  4. 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. 80–85Google Scholar
  5. 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)CrossRefGoogle Scholar
  6. 6.
    D.E. Denning, An intrusion-detection model. IEEE Trans. Softw. Eng. 13(2), 222–232 (1987)CrossRefGoogle Scholar
  7. 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. 305–316Google Scholar
  8. 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. 000159–000164Google Scholar
  9. 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. 1052–1056Google Scholar
  10. 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. 1–5Google Scholar
  11. 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. 76–81Google Scholar
  12. 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. 183–187Google Scholar
  13. 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. 310–314Google Scholar
  14. 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. 1–6Google Scholar
  15. 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. 1–6Google Scholar
  16. 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. 1–6Google Scholar
  17. 17.
    S. Ntalampiras, Detection of integrity attacks in cyber-physical critical infrastructures using ensemble modeling. IEEE Trans. Ind. Inform. 11(1), 104–111 (2015)CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 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)CrossRefGoogle Scholar
  20. 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. 140–145Google Scholar
  21. 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. 317–324Google Scholar
  22. 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)CrossRefGoogle Scholar
  23. 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. 1–7Google Scholar
  24. 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)CrossRefGoogle Scholar
  25. 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. 1–2Google Scholar
  26. 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. 1–6Google Scholar
  27. 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. 1–7Google Scholar
  28. 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. 1–6Google Scholar
  29. 29.
    M. Gu, The algorithm of information system anomaly detection, in 2013 3rd International Conference on Consumer Electronics, Communications and Networks (2013), pp. 653–657Google Scholar
  30. 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. 1–5Google Scholar
  31. 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. 64–69Google Scholar
  32. 32.
    P. Angelov, Anomaly detection based on eccentricity analysis, in 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) (2014), pp. 1–8Google Scholar
  33. 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)MathSciNetCrossRefGoogle Scholar
  34. 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)CrossRefGoogle Scholar
  35. 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)CrossRefGoogle Scholar
  36. 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. ApplGoogle Scholar
  37. 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. 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)MathSciNetCrossRefGoogle Scholar
  39. 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. 2670–2675Google Scholar
  40. 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. 004550–004555Google Scholar
  41. 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)CrossRefGoogle Scholar
  42. 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. 49–54Google Scholar
  43. 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)CrossRefGoogle Scholar
  44. 44.
    L. Sorber, M.V. Barel, L.D. Lathauwer, Structured data-fusion. IEEE J. Sel. Top. Signal Process. 9(4), 586–600 (2015)CrossRefGoogle Scholar
  45. 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)CrossRefGoogle Scholar
  46. 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)CrossRefGoogle Scholar
  47. 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)CrossRefGoogle Scholar
  48. 48.
    J. Hu, A.V. Vasilakos, Energy big data analytics and security: challenges and opportunities. IEEE Trans. Smart Grid 7(5), 2423–2436 (2016)CrossRefGoogle Scholar
  49. 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)CrossRefGoogle Scholar
  50. 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)CrossRefGoogle Scholar
  51. 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. 4871–4876Google Scholar
  52. 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)CrossRefGoogle Scholar
  53. 53.
    E. Pariser, The filter Bubble: What the Internet is Hiding from you (Penguin, UK, 2011)Google Scholar
  54. 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. 45–56Google Scholar
  55. 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. 2007–2012Google Scholar
  56. 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)CrossRefGoogle Scholar
  57. 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. 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. 1–8Google Scholar
  59. 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)CrossRefGoogle Scholar
  60. 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. 2471–2478Google Scholar
  61. 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. 401–404Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • P. Sivils
    • 1
    Email author
  • C. Rieger
    • 2
  • K. Amarasinghe
    • 1
  • M. Manic
    • 1
  1. 1.Virginia Commonwealth UniversityRichmondUSA
  2. 2.Idaho National LaboratoryIdaho FallsUSA

Personalised recommendations