IMAM - Uma ferramenta para monitoramento inteligente de sistemas e dispositivos em infraestruturas críticas de IoT
Resumo
É fundamental que sistemas de informação estejam sempre disponíveis e sejam confiáveis. Períodos de inatividade podem custar caro e ter consequências bastante significativas. A maioria das ferramentas de monitoramento conseguem identificar falhas, mas não são capazes de predizê-las e nem de realizar ações de suporte proativas. Por essa razão, este artigo apresenta o IMAM, uma ferramenta de monitoramento inteligente de sistemas capaz de utilizar técnicas de aprendizado de máquina para extrair conhecimento a partir de logs de falhas de sistemas e dispositivos em uma infraestrutura de IoT.
Referências
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Chen, Z., Xu, G., Mahalingam, V., Ge, L., Nguyen, J., Yu, W., and Lu, C. (2015). A cloud computing based network monitoring and threat detection system for critical infrastructures. Big Data Research, 3:10–23.
Faceli, K., Carvalho, A., Lorena, A. C., and Gama, J. (2011). Inteligência Artificial: Uma abordagem de aprendizado de máquina. Grupo Gen-LTC.
Ferrera, E., Conzon, D., Brizzi, P., Rossini, R., Pastrone, C., Jentsch, M., Kool, P., Kamienski, C., and Sadok, D. (2017). Xmpp-based infrastructure for iot network management and rapid services and applications development. Annals of Telecommunications, 72(7):443–457.
Halkidi, M., Batistakis, Y., and Vazirgiannis, M. (2002). Clustering validity checking methods: part ii. SIGMOD Rec., 31 (3):19–27.
Mitchell, T. M. (1997). Machine Learning. McGraw-Hill, Inc., New York, NY, USA, 1 edition.
Silva, P. T. and Torres, C. B. (2010). Gestão e Liderança Para Profissionais de Ti. 1yh edition.
Soysal, M. and Schmidt, E. G. (2010). Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison. Perform. Eval., 67(6):451–467.
Suthaharan, S. (2014). Big data classification: Problems and challenges in network intrusion prediction with machine learning. SIGMETRICS Perform. Eval. Rev., 41(4):70–73.
Tokuda, Z., Takuro, Yonezawa, and Nakazawa, J. (2014). Amonitoring dependability of city-scale iot using d-case. IEEE World Forum on Internet of Things (WF-IoT).
Webb, P., Syer, D., Long, J., Nicoll, S., Winch, R., Wilkinson, A., Overdijk, M., Dupuis, C., and Deleuze, S. (2013). Spring boot reference guide. Part IV. Spring Boot features, 24.
Witten, I. H., Frank, E., Trigg, L., Hall, M., Holmes, G., and Cunningham, S. J. (1999). Weka: Practical machine learning tools and techniques with java implementations.
Xu, R. and Wunsch, II, D. (2005). Survey of clustering algorithms. Trans. Neur. Netw., 16(3):645–678.
Yaseen, Q., Jararweh, Y., Al-Ayyoub, M., and AlDwairi, M. (2017). Collusion attacks in internet of things: Detection and mitigation using a fog based model. Sensors Applications Symposium (SAS).
Avizienis, A. (1998). Infraestructure-based design of fault-tolerant systems, volume 98.
Bass, L., Clements, P., and Kazman, R. (2003). Software Architecture in Practice. JPearson Education, Inc.,, New York, EUA, 3rd edition.
Chen, Z., Xu, G., Mahalingam, V., Ge, L., Nguyen, J., Yu, W., and Lu, C. (2015). A cloud computing based network monitoring and threat detection system for critical infrastructures. Big Data Research, 3:10–23.
Faceli, K., Carvalho, A., Lorena, A. C., and Gama, J. (2011). Inteligência Artificial: Uma abordagem de aprendizado de máquina. Grupo Gen-LTC.
Ferrera, E., Conzon, D., Brizzi, P., Rossini, R., Pastrone, C., Jentsch, M., Kool, P., Kamienski, C., and Sadok, D. (2017). Xmpp-based infrastructure for iot network management and rapid services and applications development. Annals of Telecommunications, 72(7):443–457.
Halkidi, M., Batistakis, Y., and Vazirgiannis, M. (2002). Clustering validity checking methods: part ii. SIGMOD Rec., 31 (3):19–27.
Mitchell, T. M. (1997). Machine Learning. McGraw-Hill, Inc., New York, NY, USA, 1 edition.
Silva, P. T. and Torres, C. B. (2010). Gestão e Liderança Para Profissionais de Ti. 1yh edition.
Soysal, M. and Schmidt, E. G. (2010). Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison. Perform. Eval., 67(6):451–467.
Suthaharan, S. (2014). Big data classification: Problems and challenges in network intrusion prediction with machine learning. SIGMETRICS Perform. Eval. Rev., 41(4):70–73.
Tokuda, Z., Takuro, Yonezawa, and Nakazawa, J. (2014). Amonitoring dependability of city-scale iot using d-case. IEEE World Forum on Internet of Things (WF-IoT).
Webb, P., Syer, D., Long, J., Nicoll, S., Winch, R., Wilkinson, A., Overdijk, M., Dupuis, C., and Deleuze, S. (2013). Spring boot reference guide. Part IV. Spring Boot features, 24.
Witten, I. H., Frank, E., Trigg, L., Hall, M., Holmes, G., and Cunningham, S. J. (1999). Weka: Practical machine learning tools and techniques with java implementations.
Xu, R. and Wunsch, II, D. (2005). Survey of clustering algorithms. Trans. Neur. Netw., 16(3):645–678.
Yaseen, Q., Jararweh, Y., Al-Ayyoub, M., and AlDwairi, M. (2017). Collusion attacks in internet of things: Detection and mitigation using a fog based model. Sensors Applications Symposium (SAS).
Publicado
22/07/2018
Como Citar
DOS SANTOS, David Coelho; XAVIER JÚNIOR, João Carlos; DE AQUINO JÚNIOR, Gibeon Soares.
IMAM - Uma ferramenta para monitoramento inteligente de sistemas e dispositivos em infraestruturas críticas de IoT. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 10. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 161-170.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2018.3298.
