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A Clustering Approach for Profiling LoRaWAN IoT Devices

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Ambient Intelligence (AmI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11912))

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Abstract

Internet of Things (IoT) devices are starting to play a predominant role in our everyday life. Application systems like Amazon Echo and Google Home allow IoT devices to answer human requests, or trigger some alarms and perform suitable actions. In this scenario, any data information, related device and human interaction are stored in databases and can be used for future analysis and improve the system functionality. Also, IoT information related to the network level (wireless or wired) may be stored in databases and can be processed to improve the technology operation and to detect network anomalies. Acquired data can be also used for profiling operation, in order to group devices according to their characteristics. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies in today’s world, it is a protocol based on LoRa modulation. In this work, we propose a methodology to process LoRaWAN packets and perform profiling of the IoT devices. Specifically, we use the k-means algorithm to group devices according to their radio and network behaviour. We tested our approach on a real LoRaWAN network where the entire captured traffic is stored in a proprietary database. Our analysis, performed on 286, 753 packets with 765 devices involved, leads to remarkable clustering performance according to validation indices such as the Silhouette and the Davies-Bouldin indices. Further, with the help of field-experts, we were able to analyze clusters’ contents, revealing results both in line with the current network behaviour and alerts on malfunctioning devices, remarking the reliability of the proposed approach.

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References

  1. Aceto, G., Ciuonzo, D., Montieri, A., Pescapé, A.: Mobile encrypted traffic classification using deep learning: experimental evaluation, lessons learned, and challenges. IEEE Trans. Netw. Serv. Manag. 16(2), 445–458 (2019)

    Article  Google Scholar 

  2. Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, pp. 1027–1035. Society for Industrial and Applied Mathematics, Philadelphia (2007)

    Google Scholar 

  3. Barrachina-Muñoz, S., Adame, T., Bel, A., Bellalta, B.: Towards energy efficient LPWANs through learning-based multi-hop routing. In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), pp. 644–649 (2019)

    Google Scholar 

  4. Bhatt, P., Morais, A.: Hads: hybrid anomaly detection system for IoT environments. In: 2018 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC), pp. 191–196 (2018)

    Google Scholar 

  5. Croce, D., Gucciardo, M., Tinnirello, I., Garlisi, D., Mangione, S.: Impact of spreading factor imperfect orthogonality in LoRa communications. In: Piva, A., Tinnirello, I., Morosi, S. (eds.) TIWDC 2017. CCIS, vol. 766, pp. 165–179. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67639-5_13

    Chapter  Google Scholar 

  6. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI–1(2), 224–227 (1979)

    Article  Google Scholar 

  7. Del Campo, G., Gomez, I., Sierra, S.C., Martinez, R., Santamaria, A.: Power distribution monitoring using LoRa: coverage analysis in suburban areas. In: Proceedings of the 2018 International Conference on Embedded Wireless Systems and Networks, EWSN 2018, pp. 233–238 (2018)

    Google Scholar 

  8. Di Noia, A., Martino, A., Montanari, P., Rizzi, A.: Supervised machine learning techniques and genetic optimization for occupational diseases risk prediction. Soft Comput. (2019). https://doi.org/10.1007/s00500-019-04200-2

  9. Hammerschmidt, C., Marchal, S., State, R., Pellegrino, G., Verwer, S.: Efficient learning of communication profiles from IP flow records. In: 2016 IEEE 41st Conference on Local Computer Networks (LCN), pp. 559–562 (2016)

    Google Scholar 

  10. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)

    Article  Google Scholar 

  11. Kim, D.Y., Kim, S.: Data transmission using k-means clustering in low power wide area networks with mobile edge cloud. Wirel. Pers. Commun. 105(2), 567–581 (2019)

    Article  Google Scholar 

  12. Lueth, K.L., et al.: State of the IoT & short-term outlook (2018)

    Google Scholar 

  13. Kumar, A., Lim, T.J.: Edima: early detection of IoT malware network activity using machine learning techniques. In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), pp. 289–294 (2019)

    Google Scholar 

  14. Kurniabudi, K., Purnama, B., Sharipuddin, S., Stiawan, D., Darmawijoyo, D., Budiarto, R.: Preprocessing and framework for unsupervised anomaly detection in IoT: work on progress. In: 2018 International Conference on Electrical Engineering and Computer Science (ICECOS), pp. 345–350 (2018)

    Google Scholar 

  15. Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)

    Article  MathSciNet  Google Scholar 

  16. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, vol. 1, pp. 281–297 (1967)

    Google Scholar 

  17. Marchette, D.: A statistical method for profiling network traffic. In: Proceedings of the Workshop on Intrusion Detection and Network Monitoring, pp. 119–128. USENIX Association, Berkeley (1999)

    Google Scholar 

  18. Martino, A., Giuliani, A., Rizzi, A.: Granular computing techniques for bioinformatics pattern recognition problems in non-metric spaces. In: Pedrycz, W., Chen, S.M. (eds.) Computational Intelligence for Pattern Recognition. SCI, vol. 777, pp. 53–81. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-89629-8_3

    Chapter  Google Scholar 

  19. Martino, A., Rizzi, A., Frattale Mascioli, F.M.: Efficient approaches for solving the large-scale k-medoids problem. In: Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, pp. 338–347. INSTICC, SciTePress (2017)

    Google Scholar 

  20. Martino, A., Rizzi, A., Frattale Mascioli, F.M.: Distance matrix pre-caching and distributed computation of internal validation indices in k-medoids clustering. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2018)

    Google Scholar 

  21. Martino, A., Rizzi, A., Frattale Mascioli, F.M.: Efficient approaches for solving the large-scale k-medoids problem: towards structured data. In: Sabourin, C., Merelo, J.J., Madani, K., Warwick, K. (eds.) IJCCI 2017. SCI, vol. 829, pp. 199–219. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16469-0_11

    Chapter  Google Scholar 

  22. Mostafa, B.: Monitoring internet of things networks. In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), pp. 295–298 (2019)

    Google Scholar 

  23. Muntean, V.H., Muntean, G.: A novel adaptive multimedia delivery algorithm for increasing user quality of experience during wireless and mobile e-learning. In: 2009 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, pp. 1–6 (2009)

    Google Scholar 

  24. Nivaashini, M., Thangaraj, P.: A framework of novel feature set extraction based intrusion detection system for internet of things using hybrid machine learning algorithms. In: 2018 International Conference on Computing, Power and Communication Technologies (GUCON), pp. 44–49 (2018)

    Google Scholar 

  25. Nõmm, S., Bahşi, H.: Unsupervised anomaly based botnet detection in IoT networks. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1048–1053 (2018)

    Google Scholar 

  26. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)

    Article  Google Scholar 

  27. Semtech: LoRa. EP2763321 from 2013 and U.S. Patent 7,791,415 from 2008

    Google Scholar 

  28. Sornin, N., Yegin, A., et al.: LoRaWAN 1.1 Specification (2017). https://lora-alliance.org/resource-hub/lorawantm-specification-v11

  29. Tao, M., Ming, Y.C., Juan, C.: Profiling and identifying users’ activities with network traffic analysis. In: 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp. 503–506 (2015)

    Google Scholar 

  30. Thorndike, R.L.: Who belongs in the family? Psychometrika 18(4), 267–276 (1953)

    Article  Google Scholar 

  31. Verzegnassi, E.G.M., Tountas, K., Pados, D.A., Cuomo, F.: Data conformity evaluation: a novel approach for IoT security. In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), pp. 842–846 (2019)

    Google Scholar 

  32. Zhang, H.W., Sun, L., Zhang, H.: Research on data packets clustering algorithm in the wireless multiple hop network. Appl. Mech. Mater. 651, 1905–1908 (2014)

    Article  Google Scholar 

  33. Zhang, J., Chen, J.: An adaptive clustering algorithm for dynamic heterogeneous wireless sensor networks. Wirel. Netw. 25(1), 455–470 (2019)

    Article  Google Scholar 

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Acknowledgement

We thank UNIDATA S.p.A. who provided insight and expertise that greatly assisted our research, as well the access to a subset of the data for the analysis.

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Authors and Affiliations

  1. Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, Via Eudossiana 18, 00184, Rome, Italy

    Jacopo Maria Valtorta, Alessio Martino & Francesca Cuomo

  2. University of Palermo, Palermo, Italy

    Domenico Garlisi

  3. Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), Parma, Italy

    Jacopo Maria Valtorta, Francesca Cuomo & Domenico Garlisi

Authors
  1. Jacopo Maria Valtorta
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  2. Alessio Martino
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  3. Francesca Cuomo
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  4. Domenico Garlisi
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Corresponding author

Correspondence to Domenico Garlisi .

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Editors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Ioannis Chatzigiannakis

  2. Philips Research, Eindhoven, The Netherlands

    Boris De Ruyter

  3. Hellenic Open University, Patras, Greece

    Irene Mavrommati

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Valtorta, J.M., Martino, A., Cuomo, F., Garlisi, D. (2019). A Clustering Approach for Profiling LoRaWAN IoT Devices. In: Chatzigiannakis, I., De Ruyter, B., Mavrommati, I. (eds) Ambient Intelligence. AmI 2019. Lecture Notes in Computer Science(), vol 11912. Springer, Cham. https://doi.org/10.1007/978-3-030-34255-5_5

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  • DOI: https://doi.org/10.1007/978-3-030-34255-5_5

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  • Print ISBN: 978-3-030-34254-8

  • Online ISBN: 978-3-030-34255-5

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