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Immune Approach to the Protection of IoT Devices

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10018))

Abstract

This paper presents an immune approach for securing different types of devices connected to network. This also applies to the technology, called Internet of things (IoT), which growing rapidly from year to year. It was developed to help people in everyday life, to make our life easier. However, such systems of interrelated computing devices with the ability to transfer data over a network is exposed to various types of attacks. Hacker can take the control over the each device connected to the network. As a result, for example, heating system can be switched on at the summer time, a refrigerator do redundant purchases, etc. To fix this problem, we propose to apply our hybrid immune-based algorithm, called b-v model, embedded in a reprogrammable FPGA. It base on negative selection which is suitable to protect a huge amount of devices.

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References

  1. Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 420–434. Springer, Heidelberg (2000). doi:10.1007/3-540-44503-X_27

    Chapter  Google Scholar 

  2. Balthrop, J., Esponda, F., Forrest, S., Glickman, M.: Coverage and generalization in an artificial immune system. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), New York, pp. 3–10, 9–13 July 2002

    Google Scholar 

  3. Brzozowski, M., Chmielewski, A.: Embedding the V-detector algorithm in FPGA. In: Saeed, K., Homenda, W. (eds.) CISIM 2016. LNCS, vol. 9842, pp. 43–54. Springer, Heidelberg (2016). doi:10.1007/978-3-319-45378-1_5

    Chapter  Google Scholar 

  4. Brzozowski, M., Chmielewski, A.: Hardware approach for generating b-detectors by immune-based algorithms. In: Saeed, K., Snášel, V. (eds.) CISIM 2014. LNCS, vol. 8838, pp. 615–623. Springer, Heidelberg (2014). doi:10.1007/978-3-662-45237-0_56

    Google Scholar 

  5. Chu, P.P.: RTL Hardware Design Using VHDL: Coding for Efficiency, Portability, and Scalability. Wiley-Interscience, Hoboken (2006)

    Book  Google Scholar 

  6. de Castro, L., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, London (2002)

    MATH  Google Scholar 

  7. Chmielewski, A., Wierzchoń, S.T.: On the distance norms for multidimensional dataset in the case of real-valued negative selection application. Zeszyty Naukowe Politechniki Białostockiej, No. 2, pp. 39–50 (2007)

    Google Scholar 

  8. Chmielewski, A., Wierzchoń, S.T.: Hybrid negative selection approach for anomaly detection. In: Cortesi, A., Chaki, N., Saeed, K., Wierzchoń, S. (eds.) CISIM 2012. LNCS, vol. 7564, pp. 242–253. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33260-9_21

    Chapter  Google Scholar 

  9. Dasgupta, D., Forrest, S.: Novelty detection in time series data using ideas from immunology. In: Fifth International Conference on Intelligent Systems, Reno, Nevada, 19–21 June 1996

    Google Scholar 

  10. Forrest, S., Hofmeyr, S. A., Somayaji, A., Longstaff, T. A.: A sense of self for Unix processes. In: Proceedings of the 1996 IEEE Symposium on Research in Security and Privacy, pp. 120–128. IEEE Computer Society Press (1996)

    Google Scholar 

  11. Forrest, S., Perelson, A., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: Proceedings of the IEEE Symposium on Research in Security and Privacy, Los Alamitos, pp. 202–212 (1994)

    Google Scholar 

  12. Harmer, P.K., Wiliams, P.D., Gunsch, G.H., Lamont, G.B.: Artificial immune system architecture for computer security applications. IEEE Trans. Evol. Comput. 6, 252–280 (2002)

    Article  Google Scholar 

  13. Hofmeyr, S., Forrest, S.: Architecture for an artificial immune system. Evol. Comput. J. 8(4), 443–473 (2000)

    Article  Google Scholar 

  14. Ji, Z., Dasgupta, D.: Real-valued negative selection algorithm with variable-sized detectors. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 287–298. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24854-5_30

    Chapter  Google Scholar 

  15. Ji, Z., Dasgupta, D.: Revisiting negative selection algorithms. Evol. Comput. 15(2), 223–251 (2007)

    Article  Google Scholar 

  16. Keogh, E.J., Chu, S., Hart, D., Pazzani, M.: Segmenting time series: a survey and novel approach. In: Last, M., Kandel, A., Bunke, H. (eds.) Data Mining in Time Series Databases, pp. 1–22. World Scientific, Singapore (2004)

    Chapter  Google Scholar 

  17. Sayood, K.: Introduction to Data Compression. Elsevier, Amsterdam (2005)

    MATH  Google Scholar 

  18. Stibor, T.: Phase transition and the computational complexity of generating r-contiguous detectors. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) ICARIS 2007. LNCS, vol. 4628, pp. 142–155. Springer, Heidelberg (2007). doi:10.1007/978-3-540-73922-7_13

    Chapter  Google Scholar 

  19. Vanderbauwhede, W., Benkrid, K.: High-Performance Computing Using FPGAs. Springer, New York (2013)

    Book  Google Scholar 

  20. Wierzchoń, S.T.: Generating optimal repertoire of antibody strings in an artificial immune system. In: Kłopotek, M.A., Michalewicz, M., Wierzchoń, S.T. (eds.) Intelligent Information Systems. Advances in Soft Computing, vol. 4, pp. 119–133. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  21. Wierzchoń, S.T.:Deriving concise description of non-self patterns in an artificial immune system. In: Jain, L.C., Kacprzyk, J. (eds.) New Learning Paradigm in Soft Comptuning, pp. 438-458. Physica-Verlag (2001)

    Google Scholar 

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Acknowledgment

This research was partially supported by the grants S/WI/3/13 and MB/WI/1/2014 of the Polish Ministry of Science and Higher Education.

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Correspondence to Maciej Brzozowski .

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Chmielewski, A., Brzozowski, M. (2016). Immune Approach to the Protection of IoT Devices. In: Dang, T., Wagner, R., Küng, J., Thoai, N., Takizawa, M., Neuhold, E. (eds) Future Data and Security Engineering. FDSE 2016. Lecture Notes in Computer Science(), vol 10018. Springer, Cham. https://doi.org/10.1007/978-3-319-48057-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-48057-2_5

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