Skip to main content

Advertisement

Log in

IoT6Sec: reliability model for Internet of Things security focused on anomalous measurements identification with energy analysis

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Wireless sensor and actuator devices with direct IPv6 Internet access with no human interaction compose the IP-connected Internet of Things (IoT). These devices are resource constrained in processing, memory, and energy—battery operated. IoT devices can have various applications. Although, when directly connected to the Internet they are susceptible to threats (e.g., malicious tamper of packet content to reduce the reliability of device data, the flood of requisitions for the devices to drain their energy). In this way, the literature shows the use of end-to-end security to provide confidentiality, authenticity, and integrity of IoT devices data. However, even with the benefit of secure IoT data, they are not enough to ensure reliable measurements. For this reason, this work presents a reliability model for IoT, focused on the identification of anomalous measurements (using multivariate statistics). In the experiments, we use spatial (proximity) and temporal (time interval variation) correlation, and datasets with true and false data. Additionally, we use an end-to-end secure scenario and analysis of energy consumption. The results prove the feasibility of the triad: reliability (within a system that identifies the type of the anomalous measurements), security, and low energy consumption.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Notes

  1. Device software font code (with IPSec), example keys, and host configuration files are available at https://github.com/norisjunior/IoT6Sec

  2. The normal and anomalous generated measurements, and the validation clusters (data series at all interval time) are available at https://github.com/norisjunior/IoT6Sec. In the available datasets we use the following description: AD.AP (All Devices with All Physical quantities with anomaly), 1D.AP (One device with All Physical quantities with anomalies), 1D.1P (One Device with One Physical quantity with anomaly).

  3. Used functions are available at https://github.com/norisjunior/IoT6Sec.

References

  1. Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010.

    Article  MATH  Google Scholar 

  2. Ding, Z. G., Du, D. J., & Fei, M. R. (2015). An isolation principle based distributed anomaly detection method in wireless sensor networks. International Journal of Automation and Computing, 12(4), 402–412. https://doi.org/10.1007/s11633-014-0847-9.

    Article  Google Scholar 

  3. Dunkels, A., Osterlind, F., Tsiftes, N., & He, Z. (2007). Software-based on-line energy estimation for sensor nodes. In Proceedings of the 4th workshop on embedded networked sensors—EmNets ’07. New York, NY: ACM Press, p. 28. https://doi.org/10.1145/1278972.1278979

  4. Garcia-Font, V., Carrigues, C., & Rifà-Pous, H. (2016). A comparative study of anomaly detection techniques for smart city wireless sensor networks. Sensors,. https://doi.org/10.3390/s16060868.

    Google Scholar 

  5. Granjal, J., Monteiro, E., & Silva, J. S. (2015). Security in the integration of low-power Wireless Sensor Networks with the Internet: A survey. Ad Hoc Networks, 24(PA), 264–287. https://doi.org/10.1016/j.adhoc.2014.08.001.

    Article  Google Scholar 

  6. Granjal, J., Silva, R., Monteiro, E., Sa Silva, J., & Boavida, F. (2008). Why is IPSec a viable option for wireless sensor networks. In 2008 5th IEEE international conference on mobile ad hoc and sensor systems. IEEE, pp. 802–807. https://doi.org/10.1109/MAHSS.2008.4660130.

  7. Hayajneh, T., Almashaqbeh, G., Ullah, S., & Vasilakos, A. V. (2014). A survey of wireless technologies coexistence in WBAN: Analysis and open research issues. Wireless Networks, 20(8), 2165–2199. https://doi.org/10.1007/s11276-014-0736-8.

    Article  Google Scholar 

  8. Hennebert, C., & Santos, J. D. (2014). Security protocols and privacy issues into 6LoWPAN stack: A synthesis. IEEE Internet of Things Journal, 1(5), 384–398. https://doi.org/10.1109/JIOT.2014.2359538.

    Article  Google Scholar 

  9. Hui, J., & Thubert, P. (2011). Compression format for IPv6 datagrams over IEEE 802.15.4-based networks. https://tools.ietf.org/html/rfc6282. Accessed Feb 2015.

  10. IBRL: Intel Lab Data (2004). http://db.csail.mit.edu/labdata/labdata.html.

  11. IEEE: IEEE 802.15.4, vol. 2011. IEEE WPAN TG4 (2011).

  12. Jing, Q., Vasilakos, A. V., Wan, J., Lu, J., & Qiu, D. (2014). Security of the Internet of Things: Perspectives and challenges. Wireless Networks, 20(8), 2481–2501. https://doi.org/10.1007/s11276-014-0761-7.

    Article  Google Scholar 

  13. Johnson, R. A., & Wichern, D. W. (2007). Applied multivariate statistical analysis (6th ed.). Upper Saddle River: Pearson Prentice Hall.

    MATH  Google Scholar 

  14. Kasinathan, P., Pastrone, C., Spirito, M.A., & Vinkovits, M. (2013). Denial-of-Service detection in 6LoWPAN based Internet of Things. In 2013 IEEE 9th international conference on wireless and mobile computing, networking and communications (WiMob). IEEE, pp. 600–607. https://doi.org/10.1109/WiMOB.2013.6673419.

  15. Kushalnagar, N., Montenegro, G., & Schumacher, C. (2007). IPv6 over low power wireless personal area networks (6LoWPANs): Overview, assumptions, problem statement, and goals. https://tools.ietf.org/html/rfc4919. Accessed Feb 2015.

  16. Loureiro, A. A., Nogueira, J. M. S., Ruiz, L. B., Mini, R. A. D. F., Nakamura, E. F., & Figueiredo, C. M. S. (2003). Redes de Sensores Sem Fio. In: XXI Simpósio Brasileiro de Redes de Computadores, pp. 179–226.

  17. Mayzaud, A., Badonnel, R., & Chrisment, I. (2016). A taxonomy of attacks in RPL-based internet of things. International Journal of Network Security, 18(3), 459–473.

    Google Scholar 

  18. Mingoti, S. A. (2013). Análise de dados através de métodos de estatística multivariada: uma abordagem aplicada. Belo Horizonte: Editora UFMG.

    Google Scholar 

  19. Miorandi, D., Sicari, S., De Pellegrini, F., & Chlamtac, I. (2012). Internet of things: Vision, applications and research challenges. Ad Hoc Networks, 10(7), 1497–1516. https://doi.org/10.1016/j.adhoc.2012.02.016.

    Article  Google Scholar 

  20. Morettin, P. A., & Bussab, W. O. (2004). Estatística Básica. São Paulo: Editora Saraiva.

    Google Scholar 

  21. Moshtaghi, M., Leckie, C., Karunasekera, S., Bezdek, J. C., Rajasegarar, S., & Palaniswami, M. (2011). Incremental elliptical boundary estimation for anomaly detection in wireless sensor networks. In: 2011 IEEE 11th international conference on data mining, 1, pp. 467–476. IEEE. https://doi.org/10.1109/ICDM.2011.80.

  22. Rajasegarar, S., Bezdek, J. C., Leckie, C., & Palaniswami, M. (2007). Analysis of anomalies in IBRL data from a wireless sensor network deployment. In 2007 international conference on sensor technologies and applications (SENSORCOMM 2007). IEEE, pp. 158–163. https://doi.org/10.1109/SENSORCOMM.2007.4394914.

  23. Rantos, K., Papanikolaou, A., Manifavas, C., & Papaefstathiou, I. (2013). IPv6 security for low power and lossy networks. In 2013 IFIP wireless days (WD). IEEE, pp. 1–8. https://doi.org/10.1109/WD.2013.6686510.

  24. Rassam, M., Zainal, A., & Maarof, M. (2013). Advancements of data anomaly detection research in wireless sensor networks: A survey and open issues. Sensors, 13(8), 10087–10122. https://doi.org/10.3390/s130810087.

    Article  Google Scholar 

  25. Raza, S., Duquennoy, S., Chung, T., Yazar, D., Voigt, T., & Roedig, U. (2011). Securing communication in 6LoWPAN with compressed IPsec. In 2011 international conference on distributed computing in sensor systems and workshops (DCOSS). IEEE, pp. 1–8. https://doi.org/10.1109/DCOSS.2011.5982177.

  26. Raza, S., Duquennoy, S., & Selander, G. (2016). Work-in-progress—Internet draft. Compression of IPsec AH and ESP headers for 6LoWPAN networks.

  27. Raza, S., Shafagh, H., Hewage, K., Hummen, R., & Voigt, T. (2013). Lithe: Lightweight secure CoAP for the internet of things. IEEE Sensors Journal, 13(10), 3711–3720. https://doi.org/10.1109/JSEN.2013.2277656.

    Article  Google Scholar 

  28. Raza, S., Wallgren, L., & Voigt, T. (2013). SVELTE: Real-time intrusion detection in the Internet of Things. Ad Hoc Networks, 11(8), 2661–2674. https://doi.org/10.1016/j.adhoc.2013.04.014.

    Article  Google Scholar 

  29. Shelby, Z., & Bormann, C. (2011). 6LoWPAN: The wireless embedded internet. New York: Wiley.

    Google Scholar 

  30. Silva, A. A. A., Pontes, E., Zhou, F., & Kofuji, S. T. (2014). Grey model and polynomial regression for identifying malicious nodes in MANETs. In 2014 IEEE global communications conference. IEEE, pp. 162–168. https://doi.org/10.1109/GLOCOM.2014.7036801.

  31. Suthaharan, S., Alzahrani, M., Rajasegarar, S., Leckie, C., & Palaniswami, M. (2010). Labelled data collection for anomaly detection in wireless sensor networks. In 2010 sixth international conference on intelligent sensors, sensor networks and information processing. IEEE, pp. 269–274. https://doi.org/10.1109/ISSNIP.2010.5706782.

  32. Vasseur, J. P., & Dunkels, A. (2010). Interconnecting smart objects with IP: The next internet. New York: Elsevier.

    Google Scholar 

  33. Vučinić, M., Tourancheau, B., Rousseau, F., Duda, A., Damon, L., & Guizzetti, R. (2015). Oscar: Object security architecture for the internet of things. Ad Hoc Networks, 32, 3–16. https://doi.org/10.1016/j.adhoc.2014.12.005. Internet of Things security and privacy: Design methods and optimization.

  34. Xie, M., Hu, J., & Guo, S. (2015). Segment-based anomaly detection with approximated sample covariance matrix in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26(2), 574–583. https://doi.org/10.1109/TPDS.2014.2308198.

    Article  Google Scholar 

Download references

Acknowledgements

This work is funded by the Huawei Company—project number: 2994. The project is managed by Foundation of Support to the University of São Paulo (FUSP) and Eletronic Systems Department of University of São Paulo (PSI). Number of Company / Institution Agreement: OTABRA09160202003286840274.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Norisvaldo Ferraz Junior.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ferraz Junior, N., Silva, A., Guelfi, A. et al. IoT6Sec: reliability model for Internet of Things security focused on anomalous measurements identification with energy analysis. Wireless Netw 25, 1533–1556 (2019). https://doi.org/10.1007/s11276-017-1610-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-017-1610-2

Keywords

Navigation