Mitigação de Ataques IDFs no Serviço de Agrupamento de Disseminação de Dados em Redes IoT Densas
Resumo
O crescimento da IoT vem possibilitando a criação de serviços cada vez mais personalizados, entre eles destacam-se os serviços industriais que muitas vezes lidam com massiva quantidade de dados. Porém à medida que a IoT cresce as suas ameaças são ainda maiores. Entre as ameaças estão os ataques de injeção de dados falsos (IDF) que se destacam por serem um dos mais agressivos às redes de dados como a IoT. Embora existam mecanismos que lidam com essa ameaça, eles não consideram a validação dos dados, principalmente sobre o serviço de agrupamento de dados. Este trabalho propõe um mecanismo de detecção de intrusão contra ataques IDF, chamado CONFINIT, sobre o serviço de disseminação de dados em IoT densa. Ele combina estratégias de vigilância watchdog e consenso colaborativo para a detecção de atacantes, garantindo a autenticidade dos dados coletados pelos dispositivos. O CONFINIT foi avaliado no NS-3 e obteve 99% de taxa de detecção, 3,2% de falsos negativos e 3,6% de falsos positivos, e aumentou em até 30% os agrupamentos sem ataques IDF.Referências
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Borgia, E. (2014). The internet of things vision: Key features, applications and open issues. Computer Communications, 54:1–31.
Bostami, B., Ahmed, M., and Choudhury, S. (2019). False data injection attacks in internet of things. In Performability in Internet of Things, pages 47–58. Springer.
Cervantes, C., Nogueira, M., and Santos, A. (2018). Mitigação de ataques no roteamento em iot densa e móvel baseada em agrupamento e confiabilidade dos dispositivos. In Anais do XXXVI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. SBC.
Colistra, G., Pilloni, V., and Atzori, L. (2014). Task allocation in group of nodes in the iot: A consensus approach. In 2014 IEEE International Conference on Communications (ICC), pages 3848–3853. IEEE.
Deng, R., Xiao, G., Lu, R., Liang, H., and Vasilakos, A. V. (2016). False data injection on state estimation in power systems—attacks, impacts, and defense: A survey. IEEE Transactions on Industrial Informatics, 13(2):411–423.
Gielow, F., Jakllari, G., Nogueira, M., and Santos, A. (2015). Data similarity aware dynamic node clustering in wireless sensor networks. Ad Hoc Networks, 24:29–45.
Gubbi, J., Buyya, R., Marusic, S., and Palaniswami, M. (2013). Internet of things (iot): A vision, architectural elements, and future directions. Future generation computer systems, 29(7):1645–1660.
Kailkhura, B., Brahma, S., and Varshney, P. K. (2015). Consensus based detection in the presence of data falsification attacks. arXiv preprint arXiv:1504.03413.
Kouicem, D. E., Bouabdallah, A., and Lakhlef, H. (2018). Internet of things security: A top-down survey. Computer Networks.
Kumar, A. and Pais, A. R. (2018). Deterministic en-route filtering of false reports: A combinatorial design based approach. IEEE Access, 6:74494–74505.
Kumar, S. A., Vealey, T., and Srivastava, H. (2016). Security in internet of things: Challenges, solutions and future directions. In 49th HICSS, pages 5772–5781. IEEE.
Li, B., Lu, R., Wang, W., and Choo, K.-K. R. (2017). Distributed host-based collaborative detection for false data injection attacks in smart grid cyber-physical system. Journal of Parallel and Distributed Computing, 103:32–41.
Lu, R., Lin, X., Zhu, H., Liang, X., and Shen, X. (2012). Becan: A bandwidth-efficient cooperative authentication scheme for filtering injected false data in wireless sensor networks. IEEE transactions on parallel and distributed systems, 23(1):32–43.
Mendez, D. M., Papapanagiotou, I., and Yang, B. (2017). Internet of things: Survey on security and privacy. arXiv preprint arXiv:1707.01879.
Minoli, D., Sohraby, K., and Occhiogrosso, B. (2017). Iot considerations, requirements, and architectures for smart buildings—energy optimization and next-generation building management systems. IEEE Internet of Things Journal, 4(1):269–283.
Miorandi, D., Sicari, S., De Pellegrini, F., and Chlamtac, I. (2012). Internet of things: Vision, applications and research challenges. Ad hoc networks, 10(7):1497–1516.
Mumtaz, S., Alsohaily, A., Pang, Z., Rayes, A., Tsang, K. F., and Rodriguez, J. (2017). Massive internet of things for industrial applications: Addressing wireless iiot connectivity challenges and ecosystem fragmentation. IEEE Industrial Electronics Magazine, 11(1):28–33.
Qiu, T., Chen, N., Li, K., Atiquzzaman, M., and Zhao,W. (2018). How can heterogeneous internet of things build our future: A survey. IEEE Communications Surveys & Tutorials, 20(3):2011–2027.
Sen, A. and Madria, S. (2017). Risk assessment in a sensor cloud framework using attack graphs. IEEE Transactions on Services Computing, 10(6):942–955.
Toulouse, M., Minh, B. Q., and Curtis, P. (2015). A consensus based network intrusion detection system. In 2015 5th International Conference on IT Convergence and Security (ICITCS), pages 1–6. IEEE.
UCI, C. (2013). Estatisticas de acesso web. https://archive.ics.uci.edu/ml/datasets/Gas+Sensor+Array+Drift+Dataset. Acessado em 21/05/2018.
Wang, J., Liu, Z., Zhang, S., and Zhang, X. (2014). Defending collaborative false data injection attacks in wireless sensor networks. Information Sciences, 254:39–53.
Yang, L., Ding, C., Wu, M., and Wang, K. (2017). Robust detection of false data injection attacks for data aggregation in an internet of things-based environmental surveillance. Comp. Net., 129:410–428.
Yaqoob, I., Ahmed, E., ur Rehman, M. H., Ahmed, A. I. A., Al-garadi, M. A., Imran, M., and Guizani, M. (2017). The rise of ransomware and emerging security challenges in the internet of things. Computer Networks, 129:444–458.
Yu, Z. and Guan, Y. (2010). A dynamic en-route filtering scheme for data reporting in wireless sensor networks. IEEE/ACM Transactions on Networking (ToN), 18(1):150–163.
Publicado
02/09/2019
Como Citar
PEDROSO, Carlos; GIELOW, Fernando; SANTOS, Aldri; NOGUEIRA, Michele.
Mitigação de Ataques IDFs no Serviço de Agrupamento de Disseminação de Dados em Redes IoT Densas. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 19. , 2019, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 211-224.
DOI: https://doi.org/10.5753/sbseg.2019.13973.
