Skip to main content
Log in

Design and evaluation of a user authentication model for IoT networks based on app event patterns

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Access to a variety of Internet of Things networks can be achieved through end-user devices such as smartphones or tablets. However, these devices are susceptible to theft, loss or unauthorized access. Although end-user devices are equipped with different means of authentication such as fingerprint readers, these methods are only employed at the time of access. Hence, an effective authentication mechanism that continuously authenticates users in the background is required in order to detect unauthorized access. A rich set of information can be extracted from end-user devices and utilized in the background to continuously authenticate users without requiring further intervention. As an example, the ability to continuously retrieve application usage profiles and sensor data on such devices strengthens the argument for employing behavioral-based mechanisms for continuous user authentication. This paper, which discusses behavioral-based authentication mechanisms with regard to security and usability, presents a user authentication model based on app access and network generated traffic patterns while accessing apps, utilizing a small amount of information. To validate our model, we use a public real-world dataset collected, in an uncontrolled manner, from real users over a long time period. The presented model can authenticate users with a minimum F-measure of 98%, utilizing both access time patterns and network traffic patterns. Overall, the results are promising, and the achieved high degree of accuracy proves the effectiveness and usability of the proposed model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. http://sei.pku.edu.cn/~liuxzh/appdata/.

References

  1. Deng, L., Li, D., Yao, X., Cox, D., Wang, H.: Mobile network intrusion detection for iot system based on transfer learning algorithm. Clust. Comput. 22(4), 9889–9904 (2019)

    Article  Google Scholar 

  2. Evans, D.: The Internet of Things—How the Next Evolution of the Internet is Changing Everything. CISCO White Pap., No. April, pp. 1–11 (2011)

  3. Fortino, G., Russo, W., Savaglio, C., Shen, W., Zhou, M.: Agent-oriented cooperative smart objects: from IoT system design to implementation. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1–18 (2017)

  4. Ashibani, Y., Mahmoud, Q.H.: Cyber physical systems security: analysis, challenges and solutions. J. Comput. Secur. 68, 81–97 (2017)

    Article  Google Scholar 

  5. Miloslavskaya, N., Tolstoy, A.: Internet of things: information security challenges and solutions. Clust. Comput. 22(1), 103–119 (2019)

    Article  Google Scholar 

  6. Molina, B., Palau, C.E., Fortino, G., Guerrieri, A., Savaglio, C.: Empowering smart cities through interoperable sensor network enablers. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, vol. 2014, pp. 7–12 (2014)

  7. Gajewski, M., Batalla, J.M., Mastorakis, G., Mavromoustakis, C.X.: A distributed IDS architecture model for smart home systems. Clust. Comput. 22, 1739–1749 (2019)

    Article  Google Scholar 

  8. Hosek, J., Masek, P., Kovac, D., Ries, M., Kröpfl, F.: Universal smart energy communication platform. In: IEEE 2014 International Conference on Intelligent Green Building and Smart Grid, IGBSG 2014, pp. 1–4

  9. Masek, P., Hosek, J., Ries, M., Kovac, D., Bartl, M., Kröpfl, F.: Use case study on embedded systems serving as smart home gateways. In: Recent Advances in Circuits, Systems and Automatic Control, 2013, pp. 310–315

  10. Chae, C.J., Kim, K.B., Cho, H.J.: A study on secure user authentication and authorization in OAuth protocol. Clust. Comput. 22, 1991–1999 (2019)

    Article  Google Scholar 

  11. Barcena, M.B., Wueest, C.: Insecurity in the internet of things. Security response, symantec (2016)

  12. Gheorghe, A.: The internet of things: risk in the connected home. Bitdefender (2016)

  13. Abomhara, M.: Cyber security and the internet of things: vulnerabilities, threats, intruders and attacks. J. Cyber Secur. Mobil. 4(1), 65–88 (2015)

    Article  Google Scholar 

  14. Ur, B., Jung, J., Schechter, S.: The current state of access control for smart devices in homes. In: Work from Home Usable Privacy and Security, pp. 1–6 (2014)

  15. Hewlett Packard: Internet of Things Research Study. HP p. 4 (2014)

  16. Faisal, S., Anani, N., Leiper, J., Gupta, M.: The application of everything: Canada’s apps economy value chain. The Information and Communication Technology Council (ICTC), Canada (2014)

    Google Scholar 

  17. Ashibani, Y., Mahmoud, Q.H.: A behavior profiling model for user authentication in IoT networks based on app usage patterns. In: 44th IEEE Annual Conference of the Industrial Electronics Society (IECON), pp. 2841–2846 (2018)

  18. Ashibani, Y., Mahmoud, Q.H.: A user authentication model for IoT networks based on app traffic patterns. In: 9th IEEE Annual I Information Technology; Electronics and Mobile Communication Conference (IEMCON), pp. 632–638 (2018)

  19. Zhou, K., Medsger, J., Stavrou, A., Voas, J.M.: Mobile application and device power usage measurements. In: IEEE Sixth International Conference on Software Security and Reliability (SERE), pp. 147–156 (2012)

  20. Leelavathy, J., Selvabrundha, S.: A novel approach to classify users based on keystroke behavior. Clust. Comput. 22(4), 9677–9685 (2019)

    Article  Google Scholar 

  21. Obaidat, M.S., Traore, I., Woungang, I.: Continuous authentication using writing style. In: Biometric-Based Physical and Cybersecurity Systems. Springer, Cham, pp. 211–232 (2019).

  22. Lee, W., Lee, R.B.: Multi-sensor authentication to improve smartphone security. In: IEEE International Conference on Information Systems Security and Privacy (ICISSP), pp. 1–11 (2015)

  23. Lee, W.-H., Lee, R.B.: Implicit authentication for smartphone security. In: International Conference on Information Systems Security and Privacy. Springer, pp. 160–176 (2015)

  24. Li, L., Zhao, X., Xue, G.: Unobservable re-authentication for smartphones. In: The Network and Distributed System Security Symposium, vol. 56 (2013)

  25. Trojahn, M., Ortmeier, F.: Toward mobile authentication with keystroke dynamics on mobile phones and tablets. In: International Conference on Advanced Information Networking and Applications Work, pp. 697–702 (2013)

  26. Zhu, J., Wu, P., Wang, X., Zhang, J.: SenSec: mobile security through passive sensing. In: IEEE International Conference on Computer Network Communications, pp. 1128–1133 (2013)

  27. Ashibani, Y., Kauling, D., Mahmoud, Q.H.: A context-aware authentication framework for smart homes. In: 30th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–5 (2017)

  28. Li, F., Clarke, N., Papadaki, M., Dowland, P.: Behaviour profiling for transparent authentication for mobile devices. In: European Conference on Cyber Warfare and Security Academy Conference International Limited, pp. 307–315 (2011)

  29. Shi, E., Niu, Y., Jakobsson, M., Chow, R.: Implicit authentication through learning user behavior, pp. 99–113. Springer, Berlin (2011)

    Google Scholar 

  30. Damopoulos, D., Menesidou, S.A., Kambourakis, G., Papadaki, M., Clarke, N., Gritzalis, S.: Evaluation of anomaly-based IDS for mobile devices using machine learning classifiers. Secur. Commun. Netw. 5(1), 3–14 (2012)

    Article  Google Scholar 

  31. Kalamandeen, A., Scannell, A, De Lara, E., Sheth, A., Lamarca, A.: Ensemble: cooperative proximity-based authentication, pp. 331–343 (2010)

  32. Bassu, D., Cochinwala, M., Jain, A.: A new mobile biometric based upon usage context. In: IEEE International Conference on Technologies for Homeland Security, HST, pp. 441–446 (2013)

  33. Murmuria, R., Stavrou, A., Barbará, D., Fleck, D.: Continuous authentication on mobile devices using power consumption, touch gestures and physical movement of users. In: International Workshop on Recent Advances in Intrusion Detection, Springer, Cham, pp. 405–424 (2015)

  34. Li, F., Clarke, N., Papadaki, M., Dowland, P.: Active authentication for mobile devices utilising behaviour profiling. Int. J. Inf. Secur. 13(3), 229–244 (2014)

    Article  Google Scholar 

  35. Mahbub, U., Komulainen, J., Ferreira, D., Chellappa, R.: Continuous authentication of smartphones based on application usage. IEEE Trans. Biometr. Behav. Identity Sci. 1(3), 165–180 (2019)

    Article  Google Scholar 

  36. Jose, A.C., Malekian, R., Ye, N.: Improving home automation security; integrating device fingerprinting into smart home. IEEE Access 4, 5776–5787 (2016)

    Article  Google Scholar 

  37. Prakash, A.: Continuous user authentication based score level fusion with hybrid optimization. Clust. Comput. 22(5), 12959–12969 (2019)

    Article  Google Scholar 

  38. Ashibani, Y., Mahmoud, Q.H.: A multi-feature user authentication model based on mobile app interactions. IEEE Access 8, 96322–96339 (2020)

    Article  Google Scholar 

  39. Xu, L., Zheng, X., Guo, X., Chen, G.: A cloud-based monitoring framework for smart home. In: IEEE 4th International Conference on Cloud Computing Technology and Science Proceedings, pp. 805–810 (2012)

  40. Ashibani, Y., Kauling, D., Mahmoud, Q.H.: Poster: a context-aware authentication service for smart homes. In: 14th IEEE Annual Consumer Communications & Networking Conference (CCNC), pp. 588–589 (2017)

  41. Ashibani, Y., Kauling, D., Mahmoud, Q.H.: Design and implementation of a contextual-based continuous authentication framework for smart homes. Appl. Syst. Innov. 2(1), 1–20 (2019)

    Article  Google Scholar 

  42. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  43. Massey, A., Miller, S.J.: Tests of hypotheses using statistics. Math. Dep. Brown Univ. Provid. RI 2912, 1–32 (2006)

    Google Scholar 

  44. García, V., Sánchez, J.S., Mollineda, R.A.: Knowledge-based systems on the effectiveness of preprocessing methods when dealing with different levels of class imbalance. Knowl. Syst. 25, 13–21 (2012)

    Article  Google Scholar 

  45. Amasyali, M.F., Ersoy, O.K.: Classifier ensembles with the extended space forest. IEEE Trans. Knowl. Data Eng. 26(3), 549–562 (2014)

    Article  Google Scholar 

  46. Segal, M.: Decision tree and SVM-based data analytics for theft detection in smart grid. IEEE Trans. Ind. Inform. 12(3), 1005–1016 (2016)

    Article  Google Scholar 

  47. Kim, K.S., Choi, H.H., Moon, C.S., Mun, C.W.: Comparison of K-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions. Curr. Appl. Phys. 11(3), 740–745 (2011)

    Article  Google Scholar 

  48. Hensman, J., Matthews, A., Ghahramani, Z.: Scalable variational gaussian process classification. In: 18th International Conference on Artificial Intelligence and Statistics (AISTATS) (2015)

  49. Wang, Y., Liang, Y., Sun, H., Ma, Y.: Intrusion detection and performance simulation based on improved sequential pattern mining algorithm. Clust. Comput. 8 (2020)

  50. Chen, X., Cai, X., Zhou, Y., Hao, Z.: Development of data monitoring application based on IoT. Clust. Comput. 8, 1–9 (2020)

    Google Scholar 

  51. Li, M., et al.: Coupled K-nearest centroid classification for non-IID data. In: Transactions on Computational Collective Intelligence XV, pp. 89–100 (2014)

  52. Singh, A., Prakash, S.B., Chandrasekaran, K.: A comparison of linear discriminant analysis and ridge classifier on twitter data. In: International Conference on Computing, Communication and Automation (ICCCA), pp. 133–138 (2016)

  53. Beleites, C., Neugebauer, U., Bocklitz, T., Krafft, C., Popp, J.: Sample size planning for classification models. Anal. Chim. Acta 760, 25–33 (2013)

    Article  Google Scholar 

  54. Abdi, L., Hashemi, S.: To combat multi-class imbalanced problems by means of over-sampling techniques. IEEE Trans. Knowl. Data Eng. 28(1), 238–251 (2016)

    Article  Google Scholar 

  55. Wahid, A., Rao, A.C.S.: ODRA: an outlier detection algorithm based on relevant attribute analysis method. Clust. Comput. 9, 1–17 (2020)

    Google Scholar 

  56. Qi, Y.: Random forest for bioinformatics. In: Zhang, C., Ma, Y. (eds.) Ensemble machine learning: methods and applications, pp. 307–323. Springer, New York (2012)

    Chapter  Google Scholar 

  57. Xia, J., Ghamisi, P., Yokoya, N., Iwasaki, A.: Random forest ensembles and extended multiextinction profiles for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 56(1), 202–216 (2017)

    Article  Google Scholar 

  58. López, V., Fernández, A., Moreno-Torres, J.G., Herrera, F.: Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics. Expert Syst. Appl. 39(7), 6585–6608 (2012)

    Article  Google Scholar 

Download references

Acknowledgement

The first author would like to thank the Libyan Ministry of Higher Education and Scientific Research for providing a scholarship to pursue his PhD.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yosef Ashibani.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ashibani, Y., Mahmoud, Q.H. Design and evaluation of a user authentication model for IoT networks based on app event patterns. Cluster Comput 24, 837–850 (2021). https://doi.org/10.1007/s10586-020-03156-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-020-03156-5

Keywords

Navigation