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Intelligent Data Engineering and Analytics pp 207–219Cite as

Modelling CPU Execution Time of AES Encryption Algorithm as Employed Over a Mobile Environment

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1177))

Abstract

This paper presents results on modelling of AES encryption algorithm in terms of CPU execution time, considering different modelling techniques such as linear, quadratic, cubic and exponential mathematical models, each with the application of piecewise approximations. C#.net framework is used to implement this study. This study recommends quadratic piecewise approximation modelling as the most optimized model for modelling the CPU execution time of AES towards encryption of data files. The model proposed in this study can be extended to other encryption algorithms, besides taking them over a mobile cloud environment also.

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Notes

  1. 1.

    Other parameters such as memory swap time, cache miss time can be included. However, encryption algorithm is usually in memory-resident state. Therefore, CPU execution time is the dominant parameter over all the other parameters to be considered.

  2. 2.

    This model is not considered for the comparison, as it was plotted with only three points and it yields only a quadratic equation.

References

  1. Kaliisa, R., Picard, M.: A systematic review on mobile learning in higher education: the African perspective. Turkish Online J. Educat. Technol. 16(1) (2017)

    Google Scholar 

  2. Telecommunication Union, Measuring the Information Society Report Executive summary 2018, Switzerland, Geneva, ITU Publications (2018)

    Google Scholar 

  3. Callou, G., Maciel, P., Tavares, E., Andrade, E., Nogueira, B., Araujo, C., Cunha, P.: Energy consumption and execution time estimation of embedded system applications. Microproc. Microsyst. 35 426–440 (2011) (2010), Elsevier

    Google Scholar 

  4. Toldinas, J., Damasevicius, R., Venckauskas, A., Blazauskas, T., Ceponis, J.: Energy consumption of cryptographic algorithms in mobile devices. ELEKTRONIKA IR ELEKTROTECHNIKA 20(5) (2014). ISSN 1392–1215

    Google Scholar 

  5. Montoya, A.O., Munoz, M.A., Kofuji, S.T.: Performance analysis of encryption algorithms on mobile devices. In: 47th International Carnahan Conference on Security Technology. IEEE, Colombia (2013)

    Google Scholar 

  6. Lu, C., Tseng, S.: Integrated design of AES encrypter and decrypter. In: Proceedings IEEE International Conference on Application Specific Systems, Architectures, and Processors, USA (2002)

    Google Scholar 

  7. Ramesh, A., Suruliandi, A.: Performance analysis of encryption algorithms for information security. In: International Conference on Circuits, Power and Computing Technologies. IEEE, India (2013)

    Google Scholar 

  8. Elminaam, D.S.A., Kader, H.M.A., Hadhoud, M.M.: Tradeoffs between energy consumption and security of symmetric encryption algorithms. Int. J. Comput. Theory Eng. 1(3), 1793–8201 (2009)

    Google Scholar 

  9. Javed, A., Shahid, M.A., Sharif, M., Yasmin, M.: Energy consumption in mobile phones, I. J. Comput. Netw. Informat. Secur. 12, 18–28. Modern Education and Computer Science Press

    Google Scholar 

  10. Dolezal, J., Becvar, Z.: Methodology and tool for energy consumption modeling of mobile devices. In: IEEE Wireless Communications and Networking Conference Workshops, April 2014

    Google Scholar 

  11. Marsiglio, J.: Piecewise linear approximation. https://optimization.mccormick.northwestern.edu/index.php/Piecewise_linear_approximation. Last accessed 13 Apr 2019

  12. Fallah, S.A., Arioua, M., Oualkadi, A.E., Asri, J.E.: On the performance of piecewise linear approximation techniques in WSNs. International Conference on Advanced Communication Technologies and Networking, Marrakech (2018)

    Google Scholar 

  13. Umaparvathi, M., Varughese, D.K.: Evaluation of symmetric encryption algorithms for MANETs. In: International Conference on Computational Intelligence and Computing Research. IEEE, India (2010)

    Google Scholar 

  14. Stewart, D.B.: Measuring execution time and real-time performance. In: Embedded Systems Conference, Boston (2006)

    Google Scholar 

  15. Pereira, R., Couto, M., Ribeiro, F., Cunha, J., Fernandes, J.P., Saraiva, J.: Energy efficiency across programming languages. In: Proceedings of Software Language Engineering, 12 pp. ACM, Canada (2017)

    Google Scholar 

  16. Kaufmann, J.: Reply to “What do you consider a good standard deviation?”. https://www.researchgate.net/post/What_do_you_consider_a_good_standard_deviation. Last accessed 25 Apr 2019

  17. Martin, K.G.: Assessing the fit of regression models. https://www.theanalysisfactor.com/assessing-the-fit-of-regression-models/. Last accessed 23 Apr 2019

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Author information

Authors and Affiliations

  1. BOTHO University, Gaborone, Botswana

    Ambili Thomas

  2. University of Botswana, Gaborone, Botswana

    V. Lakshmi Narasimhan

Authors
  1. Ambili Thomas
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  2. V. Lakshmi Narasimhan
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Corresponding author

Correspondence to V. Lakshmi Narasimhan .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute Industrial Technology, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  2. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

  3. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  4. School of Management, National Institute of Technology Karnataka, Surathkal, Karnataka, India

    Ritanjali Majhi

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Thomas, A., Narasimhan, V.L. (2021). Modelling CPU Execution Time of AES Encryption Algorithm as Employed Over a Mobile Environment. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_20

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