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AI and Security of Critical Infrastructure

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Abstract

The development of today’s critical infrastructure is increasingly dependent on smart technology and interconnection of networks. This introduces many vulnerabilities to cyber threats with potentially severe impacts. As such, security is a crucial concern in critical infrastructure. This chapter discusses the security concerns surrounding today’s critical infrastructure as well as the use of artificial intelligence (AI) for mitigating and preventing these threats. Varying sources of threats are defined and discussed. Furthermore, challenges associated with the use of AI are highlighted and discussed. Technical solutions tackling regularization and scalability of intelligent systems are also outlined.

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References

  1. Z. K. A. Mohammed and E. S. A. Ahmed, Internet of things applications, challenges and related future technologies. World Sci. News, 67(2), 126–148 (2017)

    Google Scholar 

  2. W. Dutton, J. Blumler, K. Kraemer, Wired Cities: Shaping the Future of Communications. Communications library (Washington Program, Annenberg School of Communications, Washington, 1987)

    Google Scholar 

  3. S. Graham, S. Marvin, Planning cybercities? Integrating telecommunications into urban planning. Town Plan. Rev. 70(1), 89–114 (1999)

    Google Scholar 

  4. T. Ishida, K. Isbister (eds.), Digital Cities: Technologies, Experiences, and Future Perspectives. Lecture Notes in Computer Science (Springer, Berlin, 2000)

    Google Scholar 

  5. R.G. Hollands, Will the real smart city please stand up? Intelligent, progressive or entrepreneurial? City Anal. Urban Trends Cult. Theory Policy Action 12(3), 303–320 (2008)

    Google Scholar 

  6. A. Greenfield, Everyware: The Dawning Age of UbiquitousComputing, 1st edn. (New Riders, Berkeley, 2006)

    Google Scholar 

  7. G. Hancke, B. Silva, G. Hancke, Jr., The role of advanced sensing in smart cities. Sensors 13, 393–425 (2012)

    Article  Google Scholar 

  8. S. Allwinkle, P. Cruickshank, Creating smart-er cities: an overview. J. Urban Technol. 18, 1–16 (2011)

    Article  Google Scholar 

  9. D. Miorandi, S. Sicari, F. De Pellegrini, I. Chlamtac, Internet of things: vision, applications and research challenges. Ad Hoc Netw. 10, 1497–1516 (2012)

    Article  Google Scholar 

  10. R. Vetter, Internet kiosk-computer-controlled devices reach the internet. Computer 28, 66 (1995)

    Article  Google Scholar 

  11. IHS, Global connected IoT devices by type 2017 and 2018. Available online at: https://www.statista.com/statistics/748737/worldwide-connected-iot-devices-by-sector/

  12. F. Firouzi, A.M. Rahmani, K. Mankodiya, M. Badaroglu, G. Merrett, P. Wong, B. Farahani, Internet-of-Things and big data for smarter healthcare: from device to architecture, applications and analytics. Futur. Gener. Comput. Syst. 78, 583–586 (2018)

    Article  Google Scholar 

  13. C. Perera, C.H. Liu, S. Jayawardena, M. Chen, A survey on internet of things from industrial market perspective. IEEE Access 2, 1660–1679 (2014)

    Article  Google Scholar 

  14. K. Gautam, V. Puri, J.G. Tromp, C.V. Le, N.G. Nguyen, Internet ofthings and healthcare technologies: a valuable synergy from design to implementation. Int. J. Mach. Learn. Netw. Collab. Eng. 2, 128–142 (2018)

    Google Scholar 

  15. J.J.P.C. Rodrigues, D.B. De Rezende Segundo, H.A. Junqueira, M.H. Sabino, R.M. Prince, J. Al-Muhtadi, V.H.C. De Albuquerque, Enabling technologies for the internet of health things. IEEE Access 6, 13129–13141 (2018)

    Article  Google Scholar 

  16. M. Hassanalieragh, A. Page, T. Soyata, G. Sharma, M. Aktas, G. Mateos, B. Kantarci, S. Andreescu, Health monitoring and management using Internet-of-Things (IoT) sensing with cloud-based processing: opportunities and challenges, in 2015 IEEE International Conference on Services Computing, New York City (IEEE, Piscataway, 2015), pp. 285–292

    Google Scholar 

  17. A. Luque-Ayala, S. Marvin, Developing a critical understanding of smart urbanism? Urban Stud. 52, 2105–2116 (2015)

    Article  Google Scholar 

  18. I. Colak, G. Fulli, S. Sagiroglu, M. Yesilbudak, C.-F. Covrig, Smart grid projects in Europe: current status, maturity and future scenarios. Appl. Energy 152, 58–70 (2015)

    Article  Google Scholar 

  19. J. Sakhnini, H. Karimipour, A. Dehghantanha, Smart grid cyber attacks detection using supervised learning and heuristic feature selection, in 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE) (2019)

    Google Scholar 

  20. M.C. Such, C. Hill, Battery energy storage and wind energy integrated into the smart grid, in 2012 IEEE PES Innovative Smart Grid Technologies (ISGT) (2012), pp. 1–4

    Google Scholar 

  21. H.M. Rouzbahani, A. Rahimnezhad, H. Karimipour, Smart households demand response management with micro grid. IEEE Innovative Smart Grid Technologies (ISGT 2019) (2019)

    Google Scholar 

  22. H. Yang, J. Zhang, J. Qiu, S. Zhang, M. Lai, Z.Y. Dong, A practical pricing approach to smart grid demand response based on load classification. IEEE Trans. Smart Grid 9, 179–190 (2018)

    Article  Google Scholar 

  23. H. Karimipour, V. Dinavahi, On false data injection attack against dynamic state estimation on smart power grids, in 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE) (2017), pp. 388–393

    Google Scholar 

  24. N. Wu, X. Li, RFID applications in cyber-physical system, in Deploying RFID – Challenges, Solutions, and Open Issues (IntechOpen, London, 2011)

    Google Scholar 

  25. D.B. Rawat, J.J.P.C. Rodrigues, I. Stojmenovic, Cyber-Physical Systems: From Theory to Practice (CRC Press, Boca Raton, 2015). Google-Books-ID: _CzSCgAAQBAJ

    Google Scholar 

  26. National Academies of Sciences, Engineering, and Medicine, A 21st Century Cyber-Physical Systems Education (The National Academies Press, Washington, 2016)

    Google Scholar 

  27. I. Lee, O. Sokolsky, Medical cyber physical systems, in Design Automation Conference (2010), pp. 743–748

    Google Scholar 

  28. A. Milenković, C. Otto, E. Jovanov, Wireless sensor networks for personal health monitoring: issues and an implementation. Comput. Commun. 29, 2521–2533 (2006)

    Article  Google Scholar 

  29. H. Karimipour, V. Dinavahi, Accelerated parallel WLS state estimation for large-scale power systems on GPU, in 2013 North American Power Symposium (NAPS) (2013), pp. 1–6

    Google Scholar 

  30. X. Fang, S. Misra, G. Xue, D. Yang, Smart grid – the new and improved power grid: a survey. IEEE Commun. Surv. Tutorials 14, 944–980 (2012)

    Article  Google Scholar 

  31. H. Karimipour, V. Dinavahi, Parallel domain decomposition based distributed state estimation for large-scale power systems, in 2015 IEEE/IAS 51st Industrial Commercial Power Systems Technical Conference (I CPS) (2015), pp. 1–5

    Google Scholar 

  32. H. Karimipour, V. Dinavahi, Extended Kalman Filter-based parallel dynamic state estimation. IEEE Trans. Smart Grid 6, 1539–1549 (2015)

    Article  Google Scholar 

  33. The Smart Grid Interoperability Panel–Smart Grid Cybersecurity Committee, Guidelines for smart grid cybersecurity, Technical Report NIST IR 7628r1, National Institute of Standards and Technology (2014)

    Google Scholar 

  34. H. Karimipour, V. Dinavahi, Robust massively parallel dynamic state estimation of power systems against cyber-attack. IEEE Access 6, 2984–2995 (2018)

    Article  Google Scholar 

  35. R. Rajkumar, I. Lee, L.R. Sha, J. Stankovic, Cyber-physical systems: the next computing revolution, in Proceedings of the 47th Design Automation Conference, DAC ’10 (2010), pp. 731–736

    Google Scholar 

  36. R. Langner, Robust Control System Networks (Momentum Press, New York, 2011)

    Google Scholar 

  37. L. Ayala, Cybersecurity for Hospitals and Healthcare Facilities – A Guide to Detection and Prevention | Luis Ayala | Apress (Apress, New York, 2016)

    Google Scholar 

  38. Z.E. Mrabet, N. Kaabouch, H.E. Ghazi, H.E. Ghazi, Cyber-security in smart grid: survey and challenges. Comput. Electr. Eng. 67, 469–482 (2018)

    Article  Google Scholar 

  39. E.K. Wang, Y. Ye, X. Xu, S.M. Yiu, L.C.K. Hui, K.P. Chow, Security issues and challenges for cyber physical system, in Proceedings of the 2010 IEEE/ACM Int’L Conference on Green Computing and Communications & Int’L Conference on Cyber, Physical and Social Computing, GREENCOM-CPSCOM ’10, Washington, pp. 733–738 (IEEE Computer Society, Washington, 2010)

    Google Scholar 

  40. Y. Shoukry, P. Martin, P. Tabuada, M. Srivastava, Non-invasive spoofing attacks for anti-lock braking systems, in Proceedings of the 15th International Conference on Cryptographic Hardware and Embedded Systems, CHES’13, Berlin (Springer, Berlin, 2013), pp. 55–72. Event-place: Santa Barbara, CA

    Google Scholar 

  41. Y. Chen, S. Kar, J.M.F. Moura, Cyber-physical attacks with control objectives. IEEE Trans. Autom. Control 63, 1418–1425 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  42. D. Papp, Z. Ma, L. Buttyan, Embedded systems security: threats, vulnerabilities, and attack taxonomy, in 2015 13th Annual Conference on Privacy, Security and Trust, PST 2015 (Institute of Electrical and Electronics Engineers Inc., Piscataway, 2015), pp. 145–152

    Google Scholar 

  43. P. Jokar, N. Arianpoo, V.C.M. Leung, Spoofing detection in IEEE 802.15.4 networks based on received signal strength. Ad Hoc Netw. 11, 2648–2660 (2013)

    Google Scholar 

  44. P.G. Neumann, Computer Related Risks (ACM Press/Addison-Wesley Publishing Co., New York, 1995)

    Google Scholar 

  45. O. Osanaiye, H. Cai, K.-K.R. Choo, A. Dehghantanha, Z. Xu, M. Dlodlo, Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing. J. Wirel. Commun. Netw. 2016, 130 (2016)

    Article  Google Scholar 

  46. Z. Su, G. Wassermann, The essence of command injection attacks in web applications, in Conference Record of the 33rd ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL ’06, New York (ACM, New York, 2006), pp. 372–382. Event-place: Charleston, South Carolina, USA

    Google Scholar 

  47. A. Souri, R. Hosseini, A state-of-the-art survey of malware detection approaches using data mining techniques. HCIS 8, 3 (2018)

    Google Scholar 

  48. J. Tian, B. Wang, X. Li, Data-driven and low-sparsity false data injection attacks in smart grid. Secur. Commun. Netw. 2018, 1–11 (2018)

    Article  Google Scholar 

  49. C. Perkins, G. Muller, Using discrete event simulation to model attacker interactions with cyber and physical security systems. Proc. Comput. Sci. 61, 221–226 (2015)

    Article  Google Scholar 

  50. M. Sweeney, C.T. Baumrucker, J.D. Burton, I. Dubrawsky, Cisco Security Professional’s Guide to Secure Intrusion Detection Systems, 1st edn. (Syngress Publishing, Mountain View, 2003)

    Google Scholar 

  51. R.U. Rehman, Intrusion Detection Systems with Snort: Advanced IDS Techniques Using Snort, Apache, MySQL, PHP, and ACID. Bruce Perens’ Open Source Series (Prentice Hall PTR, Upper Saddle River, 2003). OCLC: ocm52996780

    Google Scholar 

  52. R. Mitchell, I.-R. Chen, A survey of intrusion detection in wireless network applications. Comp. Commun. 42, 1–23 (2014)

    Article  Google Scholar 

  53. K.A. Scarfone, P.M. Mell, Guide to Intrusion Detection and Prevention Systems (IDPS). Technical Report NIST SP 800-94, National Institute of Standards and Technology, Gaithersburg (2007)

    Google Scholar 

  54. C. Alcaraz, L. Cazorla, G. Fernandez, G. Fernandez, Context-Awareness Using Anomaly-Based Detectors for Smart Grid Domains. Risks and Security of Internet and Systems (Springer, Cham, 2015)

    Book  Google Scholar 

  55. M. Naghnaeian, N. Hirzallah, P.G. Voulgaris, Dual Rate Control for Security in Cyber-physical Systems. arXiv:1504.07586 [cs] (2015)

    Google Scholar 

  56. W. Abbas, A. Laszka, Y. Vorobeychik, X. Koutsoukos, Scheduling intrusion detection systems in resource-bounded cyber-physical systems, in Proceedings of the First ACM Workshop on Cyber-Physical Systems-Security and/or PrivaCy, CPS-SPC ’15, New York (ACM, New York, 2015), pp. 55–66. Event-place: Denver, Colorado, USA

    Google Scholar 

  57. D. Kiwia, A. Dehghantanha, K.-K.R. Choo, J. Slaughter, A cyber kill chain based taxonomy of banking Trojans for evolutionary computational intelligence. J. Comput. Sci. 27, 394–409 (2018)

    Article  Google Scholar 

  58. M. Conti, T. Dargahi, A. Dehghantanha, Cyber threat intelligence: challenges and opportunities, in Cyber Threat Intelligence, ed. by A. Dehghantanha, M. Conti, T. Dargahi, Advances in Information Security (Springer International Publishing, Cham, 2018), pp. 1–6

    Google Scholar 

  59. C. T. Association, AI’s application areas in organizations 2018. Statista (2018). Available online at: https://www.statista.com/statistics/805348/world-ai-application-areas-in-enterprise/

  60. V.A. Golovko, Deep learning: an overview and main paradigms. Opt. Mem. Neural Netw. 26, 1–17 (2017)

    Article  Google Scholar 

  61. H. Karimipour, A. Dehghantanha, R.M. Parizi, K.R. Choo, H. Leung, A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids. IEEE Access 7, 80778–80788 (2019)

    Article  Google Scholar 

  62. S. Mohammadi, H. Mirvaziri, M. Ghazizadeh-Ahsaee, H. Karimipour, Cyber intrusion detection by combined feature selection algorithm. J. Inf. Secur. Appl. 44, 80–88 (2019)

    Google Scholar 

  63. H. Haddadpajouh, R. Javidan, R. Khayami, A. Dehghantanha, K.-K. Raymond Choo, A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks. IEEE Trans. Emerg. Top. Comput. PP, 1–1, 11 (2016)

    Google Scholar 

  64. L. Deng, Deep learning: methods and applications. FNT Signal Process. 7(3–4), 197–387 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  65. K. Arulkumaran, M.P. Deisenroth, M. Brundage, A.A. Bharath, Deep reinforcement learning: a brief survey. IEEE Signal Process. Mag. 34, 26–38 (2017)

    Article  Google Scholar 

  66. Y. Xin, L. Kong, Z. Liu, Y. Chen, Y. Li, H. Zhu, M. Gao, H. Hou, C. Wang, Machine learning and deep learning methods for cybersecurity. IEEE Access 6, 35365–35381 (2018)

    Article  Google Scholar 

  67. R.T. Kokila, S. Thamarai Selvi, K. Govindarajan, DDoS detection and analysis in SDN-based environment using support vector machine classifier, in 2014 Sixth International Conference on Advanced Computing (ICoAC), Chennai (IEEE, Piscataway, 2014), pp. 205–210

    Google Scholar 

  68. M. Olalere, M.T. Abdullah, R. Mahmod, A. Abdullah, Identification and evaluation of discriminative lexical features of malware URL for real-time classification, in 2016 International Conference on Computer and Communication Engineering (ICCCE), Kuala Lumpur (IEEE, Piscataway, 2016), pp. 90–95

    Google Scholar 

  69. P.-Y. Chen, S. Yang, J. A. McCann, J. Lin, X. Yang, Detection of false data injection attacks in smart-grid systems. IEEE Commun. Mag. 53, 206–213 (2015)

    Article  Google Scholar 

  70. M. Esmalifalak, L. Liu, N. Nguyen, R. Zheng, Z. Han, Detecting stealthy false data injection using machine learning in smart grid. IEEE Syst. J. 11, 1644–1652 (2017)

    Article  Google Scholar 

  71. Y. Liao, V. Vemuri, Use of K-nearest neighbor classifier for intrusion detection. Comput. Secur. 21, 439–448 (2002)

    Article  Google Scholar 

  72. A.R. Syarif, W. Gata, Intrusion detection system using hybrid binary PSO and K-nearest neighborhood algorithm, in 2017 11th International Conference on Information & Communication Technology and System (ICTS), Surabaya (IEEE, Piscataway, 2017), pp. 181–186

    Google Scholar 

  73. F. Bre, J.M. Gimenez, V.D. Fachinotti, Prediction of wind pressure coefficients on building surfaces using artificial neural networks. Energ. Build. 158, 1429–1441 (2018)

    Article  Google Scholar 

  74. W. Gao, T. Morris, B. Reaves, D. Richey, On SCADA control system command and response injection and intrusion detection,” in 2010 eCrime Researchers Summit, Dallas (IEEE, Piscataway, 2010), pp. 1–9

    Google Scholar 

  75. T. Vollmer, M. Manic, Computationally efficient Neural Network Intrusion Security Awareness, in 2009 2nd International Symposium on Resillient Control Systems, Idaho Falls (IEEE, Piscataway, 2009), pp. 25–30

    Book  Google Scholar 

  76. D. Zhu, H. Jin, Y. Yang, D. Wu, W. Chen, DeepFlow: deep learning-based malware detection by mining android application for abnormal usage of sensitive data, in 2017 IEEE Symposium on Computers and Communications (ISCC), Heraklion (IEEE, Piscataway, 2017), pp. 438–443

    Google Scholar 

  77. G. Zhao, C. Zhang, L. Zheng, Intrusion detection using deep belief network and probabilistic neural network, in 22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), Guangzhou (IEEE, Piscataway, 2017), pp. 639–642

    Google Scholar 

  78. A. Sherstinsky, Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network, arXiv:1808.03314 [cs, stat] (2018)

    Google Scholar 

  79. R. Vinayakumar, K. Soman, P. Poornachandran, S. Sachin Kumar, Detecting Android malware using Long Short-term Memory (LSTM). J. Intell. Fuzzy Syst. 34, 1277–1288 (2018)

    Article  Google Scholar 

  80. G. Loukas, T. Vuong, R. Heartfield, G. Sakellari, Y. Yoon, D. Gan, Cloud-based cyber-physical intrusion detection for vehicles using deep learning. IEEE Access 6, 3491–3508 (2018)

    Article  Google Scholar 

  81. A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems 25, ed. by F. Pereira, C.J.C. Burges, L. Bottou, K.Q. Weinberger (Curran Associates, Inc., Red Hook, 2012), pp. 1097–1105

    Google Scholar 

  82. S. Lawrence, C.L. Giles, A.C. Tsoi, A.D. Back, Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8, 98–113 (1997)

    Article  Google Scholar 

  83. X. Meng, Z. Shan, F. Liu, B. Zhao, J. Han, H. Wang, J. Wang, MCSMGS: malware classification model based on deep learning, in 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Nanjing (IEEE, Piscataway, 2017), pp. 272–275

    Book  Google Scholar 

  84. M.M.U. Chowdhury, F. Hammond, G. Konowicz, C. Xin, H. Wu, J. Li, A few-shot deep learning approach for improved intrusion detection, in 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), New York City (IEEE, Piscataway, 2017), pp. 456–462

    Google Scholar 

  85. A. Abeshu, N. Chilamkurti, Deep learning: the frontier for distributed attack detection in Fog-to-Things computing. IEEE Commun. Mag. 56, 169–175 (2018)

    Article  Google Scholar 

  86. R.C. Aygun, A.G. Yavuz, A stochastic data discrimination based autoencoder approach for network anomaly detection, in 2017 25th Signal Processing and Communications Applications Conference (SIU), Antalya (IEEE, Piscataway, 2017), pp. 1–4

    Google Scholar 

  87. M. Zolotukhin, T. Hamalainen, T. Kokkonen, J. Siltanen, Increasing web service availability by detecting application-layer DDoS attacks in encrypted traffic, in 2016 23rd International Conference on Telecommunications (ICT), Thessaloniki (IEEE, Piscataway, 2016), pp. 1–6

    Google Scholar 

  88. K. Kawaguchi, L.P. Kaelbling, Y. Bengio, Generalization in Deep Learning, arXiv:1710.05468 [cs, stat] (2017)

    Google Scholar 

  89. B. Neyshabur, S. Bhojanapalli, D. McAllester, N. Srebro, Exploring generalization in deep learning, in Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17 (Curran Associates Inc., Red Hook, 2017), pp. 5949–5958. Event-place: Long Beach, California, USA

    Google Scholar 

  90. I. Goodfellow, Y. Bengio, A. Courville, Deep Learning (MIT Press, Cambridge, 2016). http://www.deeplearningbook.org.

    MATH  Google Scholar 

  91. A. Hernández-García and P. König, Data augmentation instead of explicit regularization, arXiv:1806.03852 [cs] (2018)

    Google Scholar 

  92. A. Krogh and J.A. Hertz, A simple weight decay can improve generalization, in Advances in Neural Information Processing Systems 4, ed. by J.E. Moody, S.J. Hanson, R.P. Lippmann (Morgan-Kaufmann, Burlington, 1992), pp. 950–957

    Google Scholar 

  93. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  94. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going Deeper with Convolutions, arXiv:1409.4842 [cs] (2014)

    Google Scholar 

  95. S.J. Nowlan and G.E. Hinton, Simplifying neural networks by soft weight-sharing. Neural Comput. 4, 473–493 (1992)

    Article  Google Scholar 

  96. L. Wan, M. Zeiler, S. Zhang, Y. LeCun, R. Fergus, Regularization of neural networks using dropconnect, in Proceedings of Machine Learning Research (2013), p. 12

    Google Scholar 

  97. E.A. Smirnov, D.M. Timoshenko, S.N. Andrianov, Comparison of regularization methods for ImageNet classification with deep convolutional neural networks. AASRI Proc. 6, 89–94 (2014)

    Article  Google Scholar 

  98. C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, R. Fergus, Intriguing properties of neural networks, arXiv:1312.6199 [cs] (2013)

    Google Scholar 

  99. C.S. Wickramasinghe, D.L. Marino, K. Amarasinghe, M. Manic, Generalization of deep learning for cyber-physical system security: a survey, in IECON 2018 – 44th Annual Conference of the IEEE Industrial Electronics Society (2018), pp. 745–751

    Google Scholar 

  100. C. Zhang, S. Bengio, M. Hardt, B. Recht, O. Vinyals, Understanding deep learning requires rethinking generalization, arXiv:1611.03530 (2016)

    Google Scholar 

  101. K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556 (2014)

    Google Scholar 

  102. A. Hernandez-garcia and P. Konig, Data augmentation instead of explicit regularization, arXiv:1806.03852 (2018)

    Google Scholar 

  103. A. Kurakin, I.J. Goodfellow, S. Bengio, Adversarial examples in the physical world, arXiv:1607.02533 (2016)

    Google Scholar 

  104. Ministry of Defence, Global Strategic Trends. Swindon, England. Available online at: https://espas.secure.europarl.europa.eu/orbis/sites/default/files/generated/document/en/MinofDef_Global%20Strategic%20Trends

  105. D. Bilar, B. Saltaformaggio, Using a novel behavioral stimuli-response framework to defend against adversarial cyberspace participants, in 2011 3rd International Conference on Cyber Conflict (2011), pp. 1–16

    Google Scholar 

  106. E. Tyugu, Command and control of cyber weapons, in 2012 4th International Conference on Cyber Conflict (CYCON 2012) (2012) pp. 1–11

    Google Scholar 

  107. A. Guarino, Autonomous intelligent agents in cyber offence, in 2013 5th International Conference on Cyber Conflict (CYCON 2013) (2013), pp. 1–12

    Google Scholar 

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Sakhnini, J., Karimipour, H., Dehghantanha, A., Parizi, R.M. (2020). AI and Security of Critical Infrastructure. In: Choo, KK., Dehghantanha, A. (eds) Handbook of Big Data Privacy. Springer, Cham. https://doi.org/10.1007/978-3-030-38557-6_2

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