Avaliação de Desempenho de Modelos Deep Learning para Deteção de Intrusão em Dispositvos IoT
Resumo
Com o alto crescimento do número de dispositivos conectados à Internet das Coisas (IoT), a segurança digital tornou-se um dos principais pontos a serem tratados. Muitos desses dispositivos utilizam tecnologias defasadas, as quais criam vulnerabilidades que podem ser exploradas por criminosos. Novas técnicas de segurança surgem através da utilização de modelos de Inteligência Artificial para melhorar antigas implementações, por exemplo, os sistemas de detecção de intrusão baseados em Deep Learning para identificar ataques cibernéticos. No entanto, os trabalhos existentes não avaliam os impactos desses sistemas no desempenho de ambientes com restrições computacionais, tais como os dispositivos de IoT. Assim, este trabalho objetiva avaliar o desempenho de dois modelos de detecção de intrusão baseados em Deep Learning desenvolvidos para ambientes de IoT. Os resultados revelam que diferentes modelos têm diferentes impactos no desempenho dos dispositivos de IoT. Adicionalmente, o modelo que recebe um maior volume de ataques, e possui maior precisão, consome mais recursos, o que pode ser bastante problemático para os ambientes de IoT.
Palavras-chave:
Internet das Coisas, Deep Learning, Sistema de Detecção de Intrusão, Avaliação de Desempenho
Referências
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Din, I. U., Guizani, M., Rodrigues, J. J., Hassan, S., and Korotaev, V. V. (2019). Machine learning in the internet of things: Designed techniques for smart cities. Future Generation Computer Systems, 100:826–843.
Dürr, O., Pauchard, Y., Browarnik, D., Axthelm, R., and Loeser, M. (2015). Deep learning on a raspberry pi for real time face recognition. In Eurographics (Posters), pages 11–12
Eremia, M., Toma, L., and Sanduleac, M. (2017). The smart city concept in the 21st century. Procedia Engineering, 181:12–19.
Farahnakian, F. and Heikkonen, J. (2018). A deep auto-encoder based approach for intrusion detection system. In 2018 20th International Conference on Advanced Communication Technology (ICACT), pages 178–183. IEEE.
Ge, M., Fu, X., Syed, N., Baig, Z., Teo, G., and Robles-Kelly, A. (2019). Deep learning-based intrusion detection for iot networks. In 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC), pages 256–25609. IEEE.
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Koroniotis, N., Moustafa, N., Sitnikova, E., and Turnbull, B. (2019). Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. Future Generation Computer Systems, 100:779–796.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.
Liao, H.-J., Lin, C.-H. R., Lin, Y.-C., and Tung, K.-Y. (2013). Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications, 36(1):16–24
Lunt, T. F. (1993). A survey of intrusion detection techniques. Computers & Security, 12(4):405–418.
Martinez, F. J., Toh, C. K., Cano, J.-C., Calafate, C. T., and Manzoni, P. (2011). A survey and comparative study of simulators for vehicular ad hoc networks (vanets). Wireless Communications and Mobile Computing, 11(7):813–828.
Miraz, M. H., Ali, M., Excell, P. S., and Picking, R. (2015). A review on internet of things (iot), internet of everything (ioe) and internet of nano things (iont). In 2015 Internet Technologies and Applications (ITA), pages 219–224. IEEE.
Morabito, R. (2017). Virtualization on internet of things edge devices with container technologies: a performance evaluation. IEEE Access, 5:8835–8850.
Nurse, J. R., Creese, S., and De Roure, D. (2017). Security risk assessment in internet of things systems. IT professional, 19(5):20–26.
Pannu, A. (2015). Artificial intelligence and its application in different areas. Artificial Intelligence, 4(10):79–84.
Quincozes, S. E., Kazienko, J. F., and Copetti, A. (2018). Avaliação de conjuntos de atributos para a detecção de ataques de personificação na internet das coisas. In Anais Estendidos do VIII Simpósio Brasileiro de Engenharia de Sistemas Computacionais. SBC.
Tama, B. A. and Rhee, K.-H. (2017). Performance evaluation of intrusion detection system using classifier ensembles. International Journal of Internet Protocol Technology, 10(1):22–29
Thamilarasu, G. and Chawla, S. (2019). Towards deep-learning-driven intrusion detection for the internet of things. Sensors, 19(9):1977.
Vinayakumar, R., Alazab, M., Soman, K., Poornachandran, P., Al-Nemrat, A., and Ven katraman, S. (2019). Deep learning approach for intelligent intrusion detection system. IEEE Access, 7:41525–41550.
Xu, P., Shi, S., and Chu, X. (2017). Performance evaluation of deep learning tools in docker containers. In 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM), pages 395–403. IEEE.
Ashton, K. (1999). An introduction to the internet of things (iot). RFID Journal.
Atzori, L., Iera, A., and Morabito, G. (2010). The internet of things: A survey. Computer networks, 54(15):2787–2805.
Chen, M., Mao, S., and Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2):171–209.
de Araujo, R. B. (2003). Computação ubı́qua: Princı́pios, tecnologias e desafios. In XXI Simpósio Brasileiro de Redes de Computadores, volume 8, pages 11–13.
Din, I. U., Guizani, M., Rodrigues, J. J., Hassan, S., and Korotaev, V. V. (2019). Machine learning in the internet of things: Designed techniques for smart cities. Future Generation Computer Systems, 100:826–843.
Dürr, O., Pauchard, Y., Browarnik, D., Axthelm, R., and Loeser, M. (2015). Deep learning on a raspberry pi for real time face recognition. In Eurographics (Posters), pages 11–12
Eremia, M., Toma, L., and Sanduleac, M. (2017). The smart city concept in the 21st century. Procedia Engineering, 181:12–19.
Farahnakian, F. and Heikkonen, J. (2018). A deep auto-encoder based approach for intrusion detection system. In 2018 20th International Conference on Advanced Communication Technology (ICACT), pages 178–183. IEEE.
Ge, M., Fu, X., Syed, N., Baig, Z., Teo, G., and Robles-Kelly, A. (2019). Deep learning-based intrusion detection for iot networks. In 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC), pages 256–25609. IEEE.
Gómez, A., Cuiñas, D., Catalá, P., Xin, L., Li, W., Conway, S., and Lack, D. (2015). Use of single board computers as smart sensors in the manufacturing industry. Procedia engineering, 132:153–159.
Koroniotis, N., Moustafa, N., Sitnikova, E., and Turnbull, B. (2019). Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. Future Generation Computer Systems, 100:779–796.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.
Liao, H.-J., Lin, C.-H. R., Lin, Y.-C., and Tung, K.-Y. (2013). Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications, 36(1):16–24
Lunt, T. F. (1993). A survey of intrusion detection techniques. Computers & Security, 12(4):405–418.
Martinez, F. J., Toh, C. K., Cano, J.-C., Calafate, C. T., and Manzoni, P. (2011). A survey and comparative study of simulators for vehicular ad hoc networks (vanets). Wireless Communications and Mobile Computing, 11(7):813–828.
Miraz, M. H., Ali, M., Excell, P. S., and Picking, R. (2015). A review on internet of things (iot), internet of everything (ioe) and internet of nano things (iont). In 2015 Internet Technologies and Applications (ITA), pages 219–224. IEEE.
Morabito, R. (2017). Virtualization on internet of things edge devices with container technologies: a performance evaluation. IEEE Access, 5:8835–8850.
Nurse, J. R., Creese, S., and De Roure, D. (2017). Security risk assessment in internet of things systems. IT professional, 19(5):20–26.
Pannu, A. (2015). Artificial intelligence and its application in different areas. Artificial Intelligence, 4(10):79–84.
Quincozes, S. E., Kazienko, J. F., and Copetti, A. (2018). Avaliação de conjuntos de atributos para a detecção de ataques de personificação na internet das coisas. In Anais Estendidos do VIII Simpósio Brasileiro de Engenharia de Sistemas Computacionais. SBC.
Tama, B. A. and Rhee, K.-H. (2017). Performance evaluation of intrusion detection system using classifier ensembles. International Journal of Internet Protocol Technology, 10(1):22–29
Thamilarasu, G. and Chawla, S. (2019). Towards deep-learning-driven intrusion detection for the internet of things. Sensors, 19(9):1977.
Vinayakumar, R., Alazab, M., Soman, K., Poornachandran, P., Al-Nemrat, A., and Ven katraman, S. (2019). Deep learning approach for intelligent intrusion detection system. IEEE Access, 7:41525–41550.
Xu, P., Shi, S., and Chu, X. (2017). Performance evaluation of deep learning tools in docker containers. In 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM), pages 395–403. IEEE.
Publicado
18/07/2021
Como Citar
CARVALHO, Valdir; QUEIROZ, Ewerton; MENDONÇA, Júlio; CALLOU, Gustavo; ANDRADE, Ermeson.
Avaliação de Desempenho de Modelos Deep Learning para Deteção de Intrusão em Dispositvos IoT. In: WORKSHOP EM DESEMPENHO DE SISTEMAS COMPUTACIONAIS E DE COMUNICAÇÃO (WPERFORMANCE), 20. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 1-12.
ISSN 2595-6167.
DOI: https://doi.org/10.5753/wperformance.2021.15718.
