Detecção do Comportamento da Névoa em Sistemas IoT

• Franklin M. Ribeiro Junior UFABC / IFMA
• Reinaldo A. C. Bianchi FEI University Center
• Carlos A. Kamienski UFABC

DOI: https://doi.org/10.5753/courb.2022.223453

Abstract

A fog-based IoT system has thousands of heterogeneous devices with distinct constraints. In this article, we propose a system that uses machine learning to group these devices' behaviors and identify each fog node's performance anomalies. We simulate different behaviors to evaluate our system, using the MeanShift, BIRCH, and K-Means clustering algorithms. We also evaluate the clustering data models using Silhouette, Davies-Bouldin, and Calinski-Harabasz indexes. We observe that the system identifies simulated behaviors with at least 99% accuracy, using the K-Means algorithm and the Calinski-Harabasz index in clustering validation.

References

Al-amri R., Murugesan R.K., Man M., Abdulateef A.F., Al-Sharafi M.A., Alkahtani A.A. (2021) "A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data". Applied Sciences. 11(12), 5320.

Apostol I., Preda M., Nila C., and Bica I., (2021) "IoT Botnet Anomaly Detection Using Unsupervised Deep Learning," Electronics, vol. 10, no. 16.

Atlam H., Walters R., and Wills G. (2018) "Fog Computing and the Internet of Things: A Review," Big Data and Cognitive Computing, vol. 2, no. 2, p. 10.

Atzori, L., Iera, A. and Morabito, G. (2017) "Understanding the Internet of Things: definition, potentials, and societal role of a fast evolving paradigm". Ad Hoc Networks, 56, pp.122-140.

Cheng Y. (1995) "Mean shift, mode seeking, and clustering", in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 8, pp. 790-799.

Cook A. A., Misirli G., and Fan Z. (2020) "Anomaly Detection for IoT Time-Series Data: A Survey", in IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6481-6494.

[Dataset] Dados do IoT Monitor (2022), https://github.com/FranklinMRJ/cenarios_iot.

Jain A. K., (2010) "Data clustering: 50 years beyond K-means", Pattern Recognition Letters, Volume 31, Issue 8.

Junior F. M. R., Kamienski C. A. (2021) "A Survey on Trustworthiness for the Internet of Things", in IEEE Access, vol. 9, pp. 42493-42514.

Linaje M., Berrocal J., Galan-Benitez A. (2019) "Mist and Edge Storage: Fair Storage Distribution in Sensor Networks", in IEEE Access, vol. 7, pp. 123860-123876.

Nõmm S., and Bahsi H. (2018) "Unsupervised Anomaly Based Botnet Detection in IoT Networks", 17th IEEE ICMLA, pp. 1048-1053, doi: 10.1109/ICMLA.2018.00171.

Power A., and Kotonya G. (2018) "A Microservices Architecture for Reactive and Proactive Fault Tolerance in IoT Systems", IEEE 19th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).

Ülü R., Xanthopoulos P. (2019) "Estimating the number of clusters in a dataset via consensus clustering", Expert Systems with Applications.

Xu, D., Tian, Y. (2015) "A Comprehensive Survey of Clustering Algorithms". Ann. Data. Sci. 2, 165-193. https://doi.org/10.1007/s40745-015-0040-1.

Xu S., Qian Y., and Hu R. Q. (2019) "A Semi-Supervised Learning Approach for Network Anomaly Detection in Fog Computing", IEEE ICC, pp. 1-6.

Yousefpour A., Fung C., Nguyen T., Kadiyala K., Jalali F., Niakanlahiji A., Kong J., Jue J.P. (2019) "All one needs to know about fog computing and related edge computing paradigms: A complete survey", Journal of Systems Architecture.

Zhang T., Ramakrishnan R., Livny M. (1997) "BIRCH: A New Data Clustering Algorithm and Its Applications". Data Mining and Knowledge Discovery 1.

Zyrianoff, I. D. R., Borelli F., Kamienski C. (2017) "SenSE? Sensor Simulation Environment: Uma ferramenta para geração de tráfego IoT em larga escala". Simpósio Brasileiro de Redes e Sistemas Distribuídos (SBRC).

Zyrianoff I., Heideker A., Silva D., Kleinschmidt J., Soininen J.-P., Cinotti S.T., and Kamienski C. (2019) "Architecting and Deploying IoT Smart Applications: A Performance-Oriented Approach," Sensors, vol. 20, no. 1, p. 84.