Abstract
This research explores the capacity of Machine Learning techniques to detect anomalies and how incorporate this capacity to thinger.io platform. Thinger.io is a IoT opensource platform that allows to create an IoT environment using any hardware available on market. In this paper, several ML techniques are proposed to detect anomalies in the platform.
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References
Abbasi, A.Z., Islam, N., Shaikh, Z.A., et al.: A review of wireless sensors and networks’ applications in agriculture. Comput. Stand. Interfaces 36(2), 263–270 (2014)
Alemdar, H., Ersoy, C.: Wireless sensor networks for healthcare: a survey. Comput. Netw. 54(15), 2688–2710 (2010)
Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)
Augusto, J., Shapiro, D.: Advances in Ambient Intelligence, vol. 164. IOS Press Inc., Amsterdam (2007)
Aziz, A., Salama, M., ella Hassanien, A., El-Ola Hanafi, S.: Detectors generation using genetic algorithm for a negative selection inspired anomaly network intrusion detection system. In: Proceedings of the Federated Conference on Computer Science and Information Systems, pp. 597–602, September 2012
Borrajo, M.L., Baruque, B., Corchado, E., Bajo, J., Corchado, J.M.: Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises. Int. J. Neural Syst. 21(04), 277–296 (2011)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)
Chapelle, O., Schlkopf, B., Zien, A.: Semi-Supervised Learning, 1st edn. MIT Press, Cambridge (2010)
Dasgupta, D., Niño, L.F.: Immunological Computation: Theory and Applications. CRC Press, Boca Raton (2009)
De Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Comput. 6(3), 239–251 (2002)
Ducatel, K., Bogdanowicz, M., Scapolo, F., Leijten, J., Burgelman, J.: Scenarios for ambient intelligence 2010, ISTAG Report, European Commission. Institute for Prospective Technological Studies, Seville (2001). ftp://ftp.cordis.lu/pub/ist/docs/istagscenarios2010.pdf
Eskin, E., Arnold, A., Prerau, M., Portnoy, L., Stolfo, S.: A geometric framework for unsupervised anomaly detection. In: Barbará, D., Jajodia, S. (eds.) Applications of data mining in computer security. ADIS, vol. 6, pp. 77–101. Springer, Boston (2002). https://doi.org/10.1007/978-1-4615-0953-0_4
Fisher, D.K., Fletcher, R.S., Anapalli, S.S., Pringle III, H.: Development of an open-source cloud-connected sensor-monitoring platform. Adv. Internet Things 8(01), 1 (2017)
Florez, J., Rojas, J., López, D.: Evaluación de tecnologías de comunicación para redes vehiculares de última generación. Redes de Ingeniería 1(1), 12–23 (2012)
Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)
Ji, Z., Dasgupta, D.: Real-valued negative selection algorithm with variable-sized detectors. In: Deb, K. (ed.) GECCO 2004. LNCS, vol. 3102, pp. 287–298. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24854-5_30
Kamar, I., Chatterjee, P., Hamie, A.: Internet of things in learning systems-a perspective of platforms. Int. J. Adv. Res. Comput. Sci. 7(2), 52–56 (2016)
Kim, J., Lee, J., Kim, J., Yun, J.: M2M service platforms: survey, issues, and enabling technologies. IEEE Commun. Surv. Tutor. 16(1), 61–76 (2014)
King, S., King, D., Astley, K., Tarassenko, L., Hayton, P., Utete, S.: The use of novelty detection techniques for monitoring high-integrity plant. In: Proceedings of the 2002 International Conference on Control Applications, vol. 1, pp. 221–226. IEEE (2002)
La Ode Hasnuddin, S.S., Abidin, M.S.: Internet of things for early detection of lanslides. In: Prosiding Seminar Nasional Riset Kuantitatif Terapan 2017, vol. 1 (2018)
Likotiko, E., Petrov, D., Mwangoka, J., Hilleringmann, U.: Real time solid waste monitoring using cloud and sensors technologies. Online J. Sci. Technol. 8(1), 106–116 (2018)
Martí, L., Fansi-Tchango, A., Navarro, L., Schoenauer, M.: Anomaly detection with the voronoi diagram evolutionary algorithm. In: Handl, J., Hart, E., Lewis, P.R., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds.) PPSN 2016. LNCS, vol. 9921, pp. 697–706. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45823-6_65
Martí, L., Fansi Tchango, A., Navarro, L., Schoenauer, M.: VorAIS: a multi-objective voronoi diagram-based artificial immune system. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, pp. 11–12. ACM (2016)
Miorandi, D., Sicari, S., De Pellegrini, F., Chlamtac, I.: Internet of things: vision, applications and research challenges. Ad Hoc Netw. 10(7), 1497–1516 (2012)
Pantazis, N.A., Nikolidakis, S.A., Vergados, D.D.: Energy-efficient routing protocols in wireless sensor networks: a survey. IEEE Commun. Surv. Tutor. 15(2), 551–591 (2013)
Patel, S., Park, H., Bonato, P., Chan, L., Rodgers, M.: A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil. 9(1), 21 (2012)
Shafi, K., Abbass, H.A.: Biologically-inspired complex adaptive systems approaches to network intrusion detection. Inf. Secur. Tech. Rep. 12(4), 209–217 (2007)
Sudevalayam, S., Kulkarni, P.: Energy harvesting sensor nodes: survey and implications. IEEE Commun. Surv. Tutor. 13(3), 443–461 (2011)
Suo, H., Wan, J., Zou, C., Liu, J.: Security in the internet of things: a review. In: 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE), vol. 3, pp. 648–651. IEEE (2012)
Suryadevara, N., Gaddam, A., Rayudu, R., Mukhopadhyay, S.: Wireless sensors network based safe home to care elderly people: behaviour detection. Sens. Actuators A Phys. 186, 277–283 (2012)
Weber, R.H.: Internet of things-new security and privacy challenges. Comput. Law Secur. Rev. 26(1), 23–30 (2010)
Woźniak, M., Graña, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16, 3–17 (2014). Special Issue on Information Fusion in Hybrid Intelligent Fusion Systems, http://www.sciencedirect.com/science/article/pii/S156625351300047X
Acknowledgements
This work was supported in part by Project MINECO TEC2017-88048-C2-2-R, FAPERJ APQ1 Project 211.500/2015, FAPERJ APQ1 Project 211.451/2015, CNPq Universal 430082/2016-9, FAPERJ JCNE E-26/203.287/2017, Project Prociência 2017-038625-0, CNPq PQ 312792/2017-4.
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Sanchez-Pi, N., Martí, L., Bustamante, Á.L., Molina, J.M. (2018). How Machine Learning Could Detect Anomalies on Thinger.io Platform?. In: Bajo, J., et al. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Communications in Computer and Information Science, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-319-94779-2_23
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