Reliability, Fault Tolerance and Other Critical Components for Survivability in Information Warfare

  • Peter StavroulakisEmail author
  • Maryna Kolisnyk
  • Vyacheslav Kharchenko
  • Nikolaos Doukas
  • Oleksandr P. Markovskyi
  • Nikolaos G. Bardis
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 990)


The information revolution has caused many aspects of human activity to critically depend on a wide variety of physically existing or virtual technological achievements such as electronic devices, computer systems, algorithms, cloud resources, artificial intelligence hardware and software entities etc. Many of these systems are used in highly sensitive contexts, such as military applications. This implies the existence of an increasing number of unintentional disturbances or malicious attacks. Successful operation requires qualities such as robustness, fault tolerance, reliability, availability and security. All these may be summarized by the title of survivability. Survivability of critical systems working for sensitive applications involves the ability to provide uninterrupted operation under severe disturbances, gracefully degrade when limiting conditions are reached and maintain the ability to resume normal service once the disturbances have been removed. Survivability is an important, even - though non – functional, lifecycle property of many engineering systems. Further desirable elements of survivability include the ability of systems to recognize and resist attacks or accidents, adapt in order to avoid them and modify their behavior in order to diminish the effects of similar future occurrences. This chapter presents a quantitative approach to assessing survivability and an account of survivability in military systems. A scheme for survivability via replica diversity in the implementation of the AES algorithm is then presented. Following that, an algorithm for adaptive attack aversion in user authentication systems is presented that is based on Boolean transformations. An approach for increased survivability in Internet of Things (IoT) systems is then presented. Finally, an algorithm for secure data storage in cloud resources is presented that allows attack detection and avoidance.


  1. 1.
    Stavroulakis, P.: Reliability, Survivability and Quality of Large Scale Telecommunication Systems. Wiley, London (2003)Google Scholar
  2. 2.
    Ellison, R.J., et al.: Survivable network system: an emerging discipline. Technical report, CMU/SEI-97- TR-013. Pittsburgh, PA: Software Engineering Institute, Carnegie Mellon University, November 1997Google Scholar
  3. 3.
    Dou, B.-L., Wang, X.-G., Zhang, S.-Y.: Research on survivability of networked information system. In: 2009 International Conference on Signal Processing Systems (2009)Google Scholar
  4. 4.
    Liu, Y., Trivedi, K.S.: Survivability quantification: the analytical modeling approach. Int. J. Performability Eng. 2(1), 29–44 (2006)Google Scholar
  5. 5.
    Heegaard, P.E., Trivedi, K.S.: Survivability quantification of communication services. In: International Conference on Dependable Systems & Networks: Anchorage, Alaska (2008)Google Scholar
  6. 6.
    Knight, J.C., Strunk, E.A., Sullivan, K.J.: Towards a rigorous definition of information system survivability. In: 2003 Proceedings DARPA Information Survivability, Conference and Exposition (2003)Google Scholar
  7. 7.
    Bardis, N.G., Doukas, N., Markovskyi, O.P.: Organization of the polymorphic implementation of Rijndael on microcontrollers and smart cards. In: MILCOM 2010 Military Communications Conference. IEEE (2010)Google Scholar
  8. 8.
    Kocher, P., Jaffe, J., Jun, B.: Differential power analysis. In: Wiener, M. (ed.) CRYPTO 1999. LNCS, vol. 1666, pp. 388–397. Springer, Heidelberg (1999). Scholar
  9. 9.
    Akkar, M.-L., Giraud, C.: An implementation of DES and AES, secure against some attacks. In: Koç, Ç.K., Naccache, D., Paar, C. (eds.) CHES 2001. LNCS, vol. 2162, pp. 309–318. Springer, Heidelberg (2001). Scholar
  10. 10.
    Stavroulakis, P., Markovskyi, O.P., Bardis, N.G., Doukas, N.: Efficient zero—knowledge identification based on one way Boolean transformations. In: 2011 IEEE GLOBECOM Workshops, pp. 275–280. IEEE (2011)Google Scholar
  11. 11.
    Schneier, B.: Applied Cryptography: Protocols, Algorithms and Source codes in C, 758 p. Wiley, New York (1995)Google Scholar
  12. 12.
    Kurosawa, K., Yoshida, T.: Strongly universal hashing and identification codes via channels. IEEE Trans. Inf. Theory 45(6), 2091–2095 (1999)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Seberry, J., et al.: Nonlinearity and propagation characteristics of balanced Boolean functions. Inf. Comput. 119(1), 1–13 (1995)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Feige, U., Fiat, A., Shamir, A.: Zero knowledge proofs of identity. J. Cryptol. 1(2), 77–94 (1987)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Βardis, N.G., Polymenopoulos, A., Bardis, E.G., Markovskyy, A.P.: Methods for increasing the efficiency of the remote user authentication in integrated systems. Trends Comput. Sci. 12(1), 99–107 (2003). ISBN 1-59454-065-9Google Scholar
  16. 16.
    Braz, C., Robert, J.M.: Security and usability: the case of the user authentication methods. In: Proceedings of the 18th International Conference of the Association Francophone d’Interaction Homme-Machine, pp. 199–203 (2006)Google Scholar
  17. 17.
    Wang, H., Sheng, B, Tan, C., Qun, L.: Comparing symmetric-key and public-key based security schemes in sensor networks: a case study of user access control. In: Proceedings of the 28th International Conference on Distributed Computing Systems, pp. 11–18 (2008)Google Scholar
  18. 18.
    Tsai, J.-L.: Efficient multi-server authentication scheme based on one-way hash function without verification table. Comput. Secur. 27(3–4), 115–121 (2008)CrossRefGoogle Scholar
  19. 19.
    Bardis, N.G., Doukas, N., Markovskyi, O.: Two level efficient user authentication scheme. In: Proceedings of the 4th IEEE International Conference on Digital Ecosystems and Technology, 12–15 April 2010, Knowledge Village, Dubai, UAE (2010)Google Scholar
  20. 20.
    Bardis, N., Doukas, N., Markovskyi, O.: Fast subscriber identification based on the zero knowledge principle for multimedia content distribution. Int. J. Multimed. Intell. Secur. (2010)Google Scholar
  21. 21.
    Kharchenko, V., Kolisnyk, M., Piskachova, I., Bardis, N.: Reliability and security issues for IoT-based smart business center: architecture and Markov model. In: 2016 Third International Conference on Mathematics and Computers in Sciences and in Industry (MCSI), pp. 313–318. IEEE (2016)Google Scholar
  22. 22.
    Vermesan, O., et al.: Internet of Things – from research and innovation to market deployment. river publishers series in communication, 141 p. (2014). Accessed 3 Aug 2016
  23. 23.
  24. 24.
    NB-IOT – Enabling new business opportunities. Building a better connection. Huawei Tech. Co., Ltd.
  25. 25.
    Matat, D.: Internet rechey I tehnotrendi yak oznaki evolyutsIYi suspIlstva. Osvita Ukrayini.
  26. 26.
    Cisco IoT System Brochure Cisco IoT System Deploy. Accelerate. Innovate, 52 p. (2015).
  27. 27.
    Cisco IoT System Security: Mitigate Risk, Simplify Compliance, and Build Trust White Paper, 4 p. (2015).
  28. 28.
  29. 29.
  30. 30.
  31. 31.
    Kaspersky security bulletin 2015, 85 p. (2015). Accessed 3 Aug 2016
  32. 32.
    Internet of Things. Hewlett Packard Enterprise. Accessed 3 Aug 2016
  33. 33.
    Al-Fuqaha, M.G., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of Things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17(4), 2347–2376 (2015). Scholar
  34. 34.
    Bardis, N., Doukas, N., Markovskyi, O.P.: Effective method to restore data in distributed data storage systems. In: 2015 IEEE Military Communications Conference, MILCOM 2015. IEEE (2015)Google Scholar
  35. 35.
    Blaum, M., Hafner, J.I., Hetzler, S.: Partial MDS codes and their application to RAID type of architectures. IEEE Trans. Inf. Theory 59(7), 4510–4519 (2013)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Peterson, W.W., Weldon Jr., E.J.: Error-Correcting Codes. MIT Press, Cambridge (1984)zbMATHGoogle Scholar
  37. 37.
    Abdel-Ghaffar, K.A.S., Weber, J.H.: Parity-check matrices separating erasures from errors. IEEE Trans. Inf. Theory. 59(6), 3332–3346 (2013)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Dimakis, A.G., Prabhakaran, V., Ramchandran, K.: Decentralized Erasure Codes for Distributed Networked Storage, p. 176. University of California, Berkeley (2006)MathSciNetCrossRefGoogle Scholar
  39. 39.
    Corbett, P., et al.: Row-diagonal parity for double disk failure. In: Proceedings of the Third USENIX Conference on File and Storage Technologies, pp. 1–14 (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Peter Stavroulakis
    • 1
    Email author
  • Maryna Kolisnyk
    • 5
  • Vyacheslav Kharchenko
    • 2
  • Nikolaos Doukas
    • 3
  • Oleksandr P. Markovskyi
    • 4
  • Nikolaos G. Bardis
    • 3
  1. 1.Department of Electronic and Computer EngineeringTechnical University of Crete (TUC)ChaniaGreece
  2. 2.Department of Computer Systems and NetworksNational Aerospace University “KhAI”KharkivUkraine
  3. 3.Hellenic Army AcademyVariGreece
  4. 4.Department of Computer EngineeringNational Technical University of Ukraine, (Igor Sikorsky Kyiv Polytechnic Institute)KievUkraine
  5. 5.Department of Automation and Control in Technical SystemsNational Technical University, “KPI”KharkivUkraine

Personalised recommendations