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A Threat Analysis of Human Bond Communications

  • Geir M. KøienEmail author
Article
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Abstract

In this paper we provide a high-level threat analysis of Human Bond Communications, using the STRIDE methodology. To this end, we provide an overview over Human Bond Communications and define a sample set of cases. The Human Bond Communications cases are such that the threats literally may be existential by nature. We also outline the STRIDE threat analysis methodology, and apply it to the sample set of cases previously defined. The threat analysis is carried out at a high abstraction level to highlight the major threats.

Keywords

Human bond communications STRIDE threat analysis Security Privacy Security controls Trust 

References

  1. 1.
    Abomhara, M., Gerdes, M., & Køien, G. M. (2015). A stride-based threat model for telehealth systems. Norsk informasjonssikkerhetskonferanse (NISK), 8(1), 82–96.Google Scholar
  2. 2.
    Amunts, K., Ebell, C., Muller, J., Telefont, M., Knoll, A., & Lippert, T. (2016). The human brain project: creating a european research infrastructure to decode the human brain. Neuron, 92(3), 574–581.CrossRefGoogle Scholar
  3. 3.
    Barfield, W. (2015). Fundamentals of wearable computers and augmented reality. Boca Raton: CRC Press.CrossRefGoogle Scholar
  4. 4.
    Barker, E. (2016). Recommendation for key management: Part 1: General. Special Publication 800-57, NIST, Gaithersburg, MD 20899–8930.Google Scholar
  5. 5.
    Biggio, B., Freeman, D., Miller, B., & Sinha, A. (2017). 10th international workshop on artificial intelligence and security (aisec 2017). In Proceedings of the 2017 ACM SIGSAC conference on computer and communications security (pp. 2621–2622). ACM.Google Scholar
  6. 6.
    Billinghurst, M., Clark, A., Lee, G., et al. (2015). A survey of augmented reality. Foundations and Trends in Human-Computer Interaction, 8(2–3), 73–272.CrossRefGoogle Scholar
  7. 7.
    Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford: Oxford University Press.Google Scholar
  8. 8.
    Brandt, M., Khondoker, R., Marx, R., & Bayarou, K. (2014). Security analysis of software defined networking protocols—openflow, of-config and ovsdb. In The 2014 IEEE fifth international conference on communications and electronics (ICCE 2014), DA NANG, Vietnam.Google Scholar
  9. 9.
    Cavoukian, A. (2009). Privacy by design. Take the challenge. Information and privacy commissioner of Ontario, Canada.Google Scholar
  10. 10.
    Dauer, P., Khondoker, R., Marx, R., & Bayarou, K. (2015). Security analysis of software defined networking applications for monitoring and measurement: sflow and bigtap. In The 10th international conference on future internet (pp. 51–56). ACM.Google Scholar
  11. 11.
    De Schutter, E. (2018). Deep learning and computational neuroscience.Google Scholar
  12. 12.
    Dick, P. K. (1966). We can remember it for you wholesale. The Magazine of Fantasy and Science Fiction.Google Scholar
  13. 13.
    Dick, P. K., Shusett, R., O’Bannon, D., & Povill, J. (1990). Total recall. Movie; details at http://www.imdb.com/title/tt0100802/.
  14. 14.
    Dixit, S., & Prasad, R. (Eds.) (2017). Human bond communication: The holy grail of holistic communication and immersive experience. Wiley.  https://doi.org/10.1002/9781119341451.ch1.
  15. 15.
    Dolev, D., & Yao, A. C. (1983). On the security of public-key protocols. IEEE Transactions on Information Theory, 29(2), 198–208.MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Earnshaw, R. A. (2014). Virtual reality systems. Cambridge: Academic press.Google Scholar
  17. 17.
    Fournaris, A. P., Lampropoulos, K., & Koufopavlou, O. (2017). Hardware security for critical infrastructures - the cipsec project approach. In 2017 IEEE computer society annual symposium on VLSI (ISVLSI), (pp. 356–361).  https://doi.org/10.1109/ISVLSI.2017.69.
  18. 18.
    Gavrilovska, L., & Rakovic, V. (2016). Human bond communications: Generic classification and technology enablers. Wireless Personal Communications, 88(1), 5–21.  https://doi.org/10.1007/s11277-016-3246-4.CrossRefGoogle Scholar
  19. 19.
    Georgescu, M., Hazeyama, H., Okuda, T., Kadobayashi, Y., & Yamaguchi, S. (2016). The stride towards ipv6: A comprehensive threat model for ipv6 transition technologies. In ICISSP (pp. 243–254).Google Scholar
  20. 20.
    Grzonka, D., Jakobik, A., Kołodziej, J., & Pllana, S. (2017). Using a multi-agent system and artificial intelligence for monitoring and improving the cloud performance and security. Future Generation Computer Systems.Google Scholar
  21. 21.
    ITU-R BT.2020 (2015). Parameter values for ultra-high definition television systems for production and international programme exchange. Recommendation BT.2020, ITU, Geneva, Switzerland.Google Scholar
  22. 22.
    ITU-R BT.2100 (2017). Image parameter values for high dynamic range television for use in production and international programme exchange. Recommendation BT.2100, ITU, Geneva, Switzerland.Google Scholar
  23. 23.
    ITU-R M.2083 (2015). IMT Vision - Framework and overall objectives of the future development of IMT for 2020 and beyond. Recommendation M.2083, ITU, Geneva, Switzerland.Google Scholar
  24. 24.
    Køien, G. M. (2017). An investigation of security and privacy for human bond communications (pp. 131–172). Wiley. http://dx.doi.org/10.1002/9781119341451.ch9.
  25. 25.
    Kruger, J., & Dunning, D. (1999). Unskilled and unaware of It: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Journal of personality and social psychology, 77(6), 1121.CrossRefGoogle Scholar
  26. 26.
    Minderer, M., Harvey, C. D., Donato, F., & Moser, E. I. (2016). Neuroscience: Virtual reality explored. Nature, 533(7603), 324–325.CrossRefGoogle Scholar
  27. 27.
    National Cyber Secuirty Centre (NCSC) (2018) Secure by Default. https://www.ncsc.gov.uk/articles/secure-default. Accessed 25 Feb 2019.
  28. 28.
    O’Toole, M. T. (Ed.), (2005). Miller-Keane encyclopedia and dictionary of medicine, nursing, and allied health, 7 edn. Saunders.Google Scholar
  29. 29.
    Perrow, C. (1999). Normal accidents. Princeton: Princeton University Press.Google Scholar
  30. 30.
    Roche, J. P., & Hansen, M. R. (2015). On the horizon: Cochlear implant technology. Otolaryngologic Clinics of North America, 48(6), 1097–1116.CrossRefGoogle Scholar
  31. 31.
    Seigneur, J. M., Kölndorfer, P., Busch, M., & Hochleitner, C. (2013). A survey of trust and risk metrics for a byod mobile worker world: Third international conference on social eco-informatics.Google Scholar
  32. 32.
    Shah, S. Y., Paulovicks, B., & Zerfos, P. (2016). Data-at-rest security for spark. In Big Data (Big Data), 2016 IEEE international conference on (pp. 1464–1473). IEEE.Google Scholar
  33. 33.
    Shostack, A. (2014). Threat modeling: Designing for security (1st ed.). Hoboken: Wiley Publishing.Google Scholar
  34. 34.
    Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., et al. (2017). Mastering the game of go without human knowledge. Nature, 550(7676), 354.CrossRefGoogle Scholar
  35. 35.
    Singh, S., Okun, A., & Jackson, A. (2017). Artificial intelligence: Learning to play go from scratch. Nature, 550(7676), 336.CrossRefGoogle Scholar
  36. 36.
    Smart, N. (2017). Google infrastructure security design overview; Google cloud whitepaper. Cloud whitepaper, Google.Google Scholar
  37. 37.
    Smart, N. P., Rijmen, V., Stam, M., Warinschi, B., & Watson, G. (2014). Study on cryptographic protocols. Report TP-06-14-085-EN-N, ENISA.Google Scholar
  38. 38.
    Taleb, N. N. (2012). Antifragile: Things that gain from disorder. New York: Random House.Google Scholar
  39. 39.
    Wang, X., Ong, S. K., & Nee, A. Y. C. (2016). A comprehensive survey of augmented reality assembly research. Advances in Manufacturing, 4(1), 1–22.  https://doi.org/10.1007/s40436-015-0131-4.CrossRefGoogle Scholar
  40. 40.
    Warren, S. D., & Brandeis, L. D. (1890). The right to privacy. Harvard law review (pp. 193–220).Google Scholar
  41. 41.
    Williams, A. M., Liu, Y., Regner, K. R., Jotterand, F., Liu, P., & Liang, M. (2018). Artificial intelligence, physiological genomics, and precision medicine. Physiological Genomics.Google Scholar
  42. 42.
    Williams, I., & Yuan, X. (2015). Evaluating the effectiveness of microsoft threat modeling tool. In Proceedings of the 2015 information security curriculum development conference (p. 9). ACM.Google Scholar
  43. 43.
    Zerfos, P., Yeo, H., Paulovicks, B. D., & Sheinin, V. (2015). Sdfs: Secure distributed file system for data-at-rest security for hadoop-as-a-service. In Big Data (Big Data), 2015 IEEE international conference on (pp. 1262–1271). IEEE.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of AgderAgderNorway

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