Uma Heurística para Ofuscação de Tráfego em Internet das Coisas
Resumo
O uso de tecnologias inteligentes está cada vez mais presente no cotidiano da população devido ao avanço da Internet das Coisas (IoT). Pesquisas demonstram que invasores podem utilizar estatísticas presentes em atributos do tráfego, como tamanho dos pacotes, para inferir informações sigilosas do usuário. Na literatura, diversas abordagens de preenchimento foram propostas para proteger a privacidade do usuário contra ataques baseados em análise de tráfego. As soluções propostas inserem dados redundantes na rede, o que pode causar atrasos e sobrecargas. Esse trabalho propõem uma heurística para preenchimento de pacotes com o objetivo de mitigar ataques baseados em análise de tráfego que utilizem o tamanho dos pacotes para inferir informações do usuário. Para balancear o trade-off entre privacidade e desempenho, a heurísitica tem o objetivo de minimizar a quantidade de bytes adicionados nos pacotes. O método de preenchimento desenvolvido obteve uma melhoria de até 90% no trade-off comparada com a abordagem similar.Referências
Chen, B., Liu, Y., Zhang, S., Chen, J., and Han, Z. (2021). A survey on smart home privacy data protection technology. In 2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC), pages 583-590.
Hafeez, I., Antikainen, M., and Tarkoma, S. (2019). Protecting iot-environments against traffic analysis attacks with traffic morphing. In 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 196-201. IEEE.
Liu, H. and Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. Applied Sciences, 9(20).
Meng, T., Jing, X., Yan, Z., and Pedrycz, W. (2020). A survey on machine learning for data fusion. Information Fusion, 57:115-129.
Mohit, Ansari, S., and Kumar, A. (2021). Traffic privacy study on internet of things smart home applications. In 2021 9th International Conference on Cyber and IT Service Management (CITSM), pages 1-6.
Perera, Y., Ahmed, N., Kanhere, S., Hu, W., and Jha, S. (2022). Iot traffic obfuscation: Will it guarantee the privacy of your smart home? In ICC 2022 IEEE International Conference on Communications, pages 2954-2959.
Pinheiro, A. J., Araujo-Filho, P. F. D., Bezerra, J. D. M., and Campelo, D. R. (2021). Adaptive packet padding approach for smart home networks: A tradeoff between privacy and performance. IEEE Internet of Things Journal, 8:3930-3938.
Pinheiro, A. J., de M. Bezerra, J., Burgardt, C. A., and Campelo, D. R. (2019). Identifying iot devices and events based on packet length from encrypted traffic. Computer Communications, 144:8-17.
Poh, G. S., Gope, P., and Ning, J. (2021). Privhome: Privacy-preserving authenticated communication in smart home environment. IEEE Transactions on Dependable and Secure Computing, 18(3):1095-1107.
Rezaei, S. and Liu, X. (2019). Deep learning for encrypted traffic classification: An overview. IEEE Communications Magazine, 57(5):76-81.
Santos, B. V. d., Vergutz, A., Nogueira, M., and Macedo, R. T. (2022). Um método de ofuscaçao para proteger a privacidade no tráfego da rede iot. In Anais do XL Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 126-139. SBC.
Serror, M., Henze, M., Hack, S., Schuba, M., and Wehrle, K. (2018). Towards in-network security for smart homes. In Proceedings of the 13th International Conference on Availability, Reliability and Security, ARES 2018, New York, NY, USA. Association for Computing Machinery.
Shen, F., Zhang, S., Liu, Y., and Yang, Z. (2022). A survey of traffic obfuscation technology for smart home. In 2022 International Wireless Communications and Mobile Computing (IWCMC), pages 997-1002.
Sivanathan, A., Gharakheili, H. H., Loi, F., Radford, A., Wijenayake, C., Vishwanath, A., and Sivaraman, V. (2019). Classifying iot devices in smart environments using network traffic characteristics. IEEE Transactions on Mobile Computing, 18(8):1745-1759.
Skowron, M., Janicki, A., and Mazurczyk, W. (2020). Traffic fingerprinting attacks on internet of things using machine learning. IEEE Access, 8:20386-20400.
Tahaei, H., Afifi, F., Asemi, A., Zaki, F., and Anuar, N. B. (2020). The rise of traffic classification in iot networks: A survey. Journal of Network and Computer Applications, 154:102538.
Vale, E. R., B. Brandao, J. C., and Grivet, M. (2006). An algorithm for umts padding optimization. In 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, pages 1-5.
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., and Wang, C. (2018). Machine learning and deep learning methods for cybersecurity. IEEE Access, 6:35365-35381.
Hafeez, I., Antikainen, M., and Tarkoma, S. (2019). Protecting iot-environments against traffic analysis attacks with traffic morphing. In 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 196-201. IEEE.
Liu, H. and Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. Applied Sciences, 9(20).
Meng, T., Jing, X., Yan, Z., and Pedrycz, W. (2020). A survey on machine learning for data fusion. Information Fusion, 57:115-129.
Mohit, Ansari, S., and Kumar, A. (2021). Traffic privacy study on internet of things smart home applications. In 2021 9th International Conference on Cyber and IT Service Management (CITSM), pages 1-6.
Perera, Y., Ahmed, N., Kanhere, S., Hu, W., and Jha, S. (2022). Iot traffic obfuscation: Will it guarantee the privacy of your smart home? In ICC 2022 IEEE International Conference on Communications, pages 2954-2959.
Pinheiro, A. J., Araujo-Filho, P. F. D., Bezerra, J. D. M., and Campelo, D. R. (2021). Adaptive packet padding approach for smart home networks: A tradeoff between privacy and performance. IEEE Internet of Things Journal, 8:3930-3938.
Pinheiro, A. J., de M. Bezerra, J., Burgardt, C. A., and Campelo, D. R. (2019). Identifying iot devices and events based on packet length from encrypted traffic. Computer Communications, 144:8-17.
Poh, G. S., Gope, P., and Ning, J. (2021). Privhome: Privacy-preserving authenticated communication in smart home environment. IEEE Transactions on Dependable and Secure Computing, 18(3):1095-1107.
Rezaei, S. and Liu, X. (2019). Deep learning for encrypted traffic classification: An overview. IEEE Communications Magazine, 57(5):76-81.
Santos, B. V. d., Vergutz, A., Nogueira, M., and Macedo, R. T. (2022). Um método de ofuscaçao para proteger a privacidade no tráfego da rede iot. In Anais do XL Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 126-139. SBC.
Serror, M., Henze, M., Hack, S., Schuba, M., and Wehrle, K. (2018). Towards in-network security for smart homes. In Proceedings of the 13th International Conference on Availability, Reliability and Security, ARES 2018, New York, NY, USA. Association for Computing Machinery.
Shen, F., Zhang, S., Liu, Y., and Yang, Z. (2022). A survey of traffic obfuscation technology for smart home. In 2022 International Wireless Communications and Mobile Computing (IWCMC), pages 997-1002.
Sivanathan, A., Gharakheili, H. H., Loi, F., Radford, A., Wijenayake, C., Vishwanath, A., and Sivaraman, V. (2019). Classifying iot devices in smart environments using network traffic characteristics. IEEE Transactions on Mobile Computing, 18(8):1745-1759.
Skowron, M., Janicki, A., and Mazurczyk, W. (2020). Traffic fingerprinting attacks on internet of things using machine learning. IEEE Access, 8:20386-20400.
Tahaei, H., Afifi, F., Asemi, A., Zaki, F., and Anuar, N. B. (2020). The rise of traffic classification in iot networks: A survey. Journal of Network and Computer Applications, 154:102538.
Vale, E. R., B. Brandao, J. C., and Grivet, M. (2006). An algorithm for umts padding optimization. In 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, pages 1-5.
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., and Wang, C. (2018). Machine learning and deep learning methods for cybersecurity. IEEE Access, 6:35365-35381.
Publicado
22/05/2023
Como Citar
GONÇALVES, Francisco Thiago dos Santos; PINHEIRO, Antonio Janael; SOUZA, Críston Pereira de; BEZERRA, Jeandro de Mesquita.
Uma Heurística para Ofuscação de Tráfego em Internet das Coisas. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 41. , 2023, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 449-462.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2023.525.
