Detecção de Ataques de Botnets em IoT via Variational Autoencoder
Resumo
A Internet das Coisas (IoT) é uma tendência mundial, e sua importância na sociedade cresce com o passar dos anos. Com sua popularidade, também tornou-se importante entender a funcionalidade das redes IoT, levando em consideração requisitos relevantes como a segurança. Esse trabalho fornece um modelo de Aprendizado Profundo (DL) baseado no Variational Autoencoder (VAE), capaz de detectar anomalias no tráfego de dispositivos IoT causadas por ataques de botnets. Os experimentos conduzidos sobre um conjunto de dados conhecido, o N-BaIoT, e os resultados alcançados são comparados aos baselines de estudos recentes. Os resultados demonstram que a estratégia proposta é adequada para a tarefa de detecção de ataques e pode melhorar a segurança em ambientes IoT com respostas em baixa latência.
Referências
Alqahtani, M., Mathkour, H., & Ismail, M. M. B. (2020). Iot botnet attack detection based on optimized extreme gradient boosting and feature selection. Sensors (Switzerland), 20, 1-21.
Alsoufi, M. A., Razak, S., Siraj, M. M., Nafea, I., Ghaleb, F. A., Saeed, F., & Nasser, M. (2021). Anomaly-based intrusion detection systems in iot using deep learning: A systematic literature review. Applied Sciences (Switzerland), 11.
An, J., & Cho, S. (2015). Variational Autoencoder based Anomaly Detection using Reconstruction Probability. Special Lecture on IE, 2(1), 1-18.
Aversano, L., Bernardi, M. L., Cimitile, M., & Pecori, R. (2021). A systematic review on deep learning approaches for iot. Computer Science Review, 40.
Borges, J. B., Medeiros, J. P. S., Barbosa, L. P. A., Ramos, H. S., & Loureiro, A. A. (2022). Iot botnet detection based on anomalies of multiscale time series dynamics. IEEE Transactions on Knowledge and Data Engineering.
Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A. C., & Bengio, Y. (2015). A recurrent latent variable model for sequential data. In Advances in neural information processing systems (Vol. 28).
Gron, A. (2019). Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, Inc.
Hussain, F., Hussain, R., Hassan, S. A., & Hossain, E. (2020). Machine Learning in IoT Security: Current Solutions and Future Challenges. IEEE Communications Surveys and Tutorials, 22, 1686-1721.
Kim, J., Sim, A., Kim, J., & Wu, K. (2020). Botnet detection using recurrent variational autoencoder. In Globecom 2020 ieee global communications conference (p. 1-6).
Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations.
Kingma, D. P., & Welling, M. (2019). An Introduction to Variational Autoencoders. Foundations and Trends in Machine Learning.
Kolias, C., Kambourakis, G., Stavrou, A., & Voas, J. (2017). DDoS in the IoT: Mirai and Other Botnets. Computer, 50, 80-84.
Laguduva, V. R., Islam, S. A., Aakur, S., Katkoori, S., & Karam, R. (2019). Machine Learning Based IoT Edge Node Security Attack and Countermeasures. Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI, 670-675.
Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A., & Lloret, J. (2017). Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in iot. Sensors, 17.
Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Breitenbacher, D., Shabtai, A., & Elovici, Y. (2018). N-baiot: Network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Comput., 17, 12-22.
Mirsky, Y., Doitshman, T., Elovici, Y., & Shabtai, A. (2018). Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection. In Proc. of network and distributed system security symposium (pp. 1-15). Reston, VA: Internet Society.
Nomm, S., & Bahsi, H. (2018). Unsupervised anomaly based botnet detection in iot networks. Proceedings 17th IEEE International Conference on Machine Learning and Application, 1048-1053.
Parra, G. D. L. T., Rad, P., Choo, K.-K. R., & Beebe, N. (2020). Detecting internet of things attacks using distributed deep learning. Journal of Network and Computer Applications, 163.
Song, Y., Hyun, S., & Cheong, Y.-G. (2021). Analysis of autoencoders for network intrusion detection. Sensors, 21.
Yang, Y., Zheng, K., Wu, C., & Yang, Y. (2019). Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network. Sensors, 19.
Alsoufi, M. A., Razak, S., Siraj, M. M., Nafea, I., Ghaleb, F. A., Saeed, F., & Nasser, M. (2021). Anomaly-based intrusion detection systems in iot using deep learning: A systematic literature review. Applied Sciences (Switzerland), 11.
An, J., & Cho, S. (2015). Variational Autoencoder based Anomaly Detection using Reconstruction Probability. Special Lecture on IE, 2(1), 1-18.
Aversano, L., Bernardi, M. L., Cimitile, M., & Pecori, R. (2021). A systematic review on deep learning approaches for iot. Computer Science Review, 40.
Borges, J. B., Medeiros, J. P. S., Barbosa, L. P. A., Ramos, H. S., & Loureiro, A. A. (2022). Iot botnet detection based on anomalies of multiscale time series dynamics. IEEE Transactions on Knowledge and Data Engineering.
Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A. C., & Bengio, Y. (2015). A recurrent latent variable model for sequential data. In Advances in neural information processing systems (Vol. 28).
Gron, A. (2019). Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, Inc.
Hussain, F., Hussain, R., Hassan, S. A., & Hossain, E. (2020). Machine Learning in IoT Security: Current Solutions and Future Challenges. IEEE Communications Surveys and Tutorials, 22, 1686-1721.
Kim, J., Sim, A., Kim, J., & Wu, K. (2020). Botnet detection using recurrent variational autoencoder. In Globecom 2020 ieee global communications conference (p. 1-6).
Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations.
Kingma, D. P., & Welling, M. (2019). An Introduction to Variational Autoencoders. Foundations and Trends in Machine Learning.
Kolias, C., Kambourakis, G., Stavrou, A., & Voas, J. (2017). DDoS in the IoT: Mirai and Other Botnets. Computer, 50, 80-84.
Laguduva, V. R., Islam, S. A., Aakur, S., Katkoori, S., & Karam, R. (2019). Machine Learning Based IoT Edge Node Security Attack and Countermeasures. Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI, 670-675.
Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A., & Lloret, J. (2017). Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in iot. Sensors, 17.
Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Breitenbacher, D., Shabtai, A., & Elovici, Y. (2018). N-baiot: Network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Comput., 17, 12-22.
Mirsky, Y., Doitshman, T., Elovici, Y., & Shabtai, A. (2018). Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection. In Proc. of network and distributed system security symposium (pp. 1-15). Reston, VA: Internet Society.
Nomm, S., & Bahsi, H. (2018). Unsupervised anomaly based botnet detection in iot networks. Proceedings 17th IEEE International Conference on Machine Learning and Application, 1048-1053.
Parra, G. D. L. T., Rad, P., Choo, K.-K. R., & Beebe, N. (2020). Detecting internet of things attacks using distributed deep learning. Journal of Network and Computer Applications, 163.
Song, Y., Hyun, S., & Cheong, Y.-G. (2021). Analysis of autoencoders for network intrusion detection. Sensors, 21.
Yang, Y., Zheng, K., Wu, C., & Yang, Y. (2019). Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network. Sensors, 19.
Publicado
23/05/2022
Como Citar
CUNHA, Andressa A.; BORGES, João B.; LOUREIRO, Antonio A. F..
Detecção de Ataques de Botnets em IoT via Variational Autoencoder. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 6. , 2022, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 238-251.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2022.223573.
