Avaliação de algoritmos de machine learning para detecção de malware IoT no dataset IoT-23
Resumo
Este artigo apresenta uma avaliação de diferentes algoritmos de machine learning para detecção de malware em dispositivos IoT utilizando o dataset IoT-23. Modelos baseados nos algoritmos Random Forest, SVM, árvore de decisão e uma rede neural convolucional foram implementados e comparados. Os resultados evidenciam que o algoritmo Random Forest alcançou a maior acurácia, enquanto a rede neural convolucional e também o Random Forest obtiveram as melhores métricas de precisão e F1-Score. A metodologia de pré-processamento de dados e as métricas de avaliação são detalhadas, proporcionando uma visão abrangente da eficácia dos modelos e guiando pesquisas futuras.
Referências
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Alwashali, A. A. M. A., Abd Rahman, N. A., and Ismail, N. (2021). A survey of ransomware as a service (raas) and methods to mitigate the attack. In 2021 14th International Conference on Developments in eSystems Engineering (DeSE), pages 92–96. IEEE.
Balogh, Š., Mojžiš, J., and Krammer, P. (2022). Evaluation of system features used for malware detection. In Proceedings of the Future Technologies Conference (FTC) 2021, Volume 3, pages 46–59. Springer.
Darki, A., Faloutsos, M., Abu-Ghazaleh, N., Sridharan, M., et al. (2019). {IDAPro} for {IoT} malware analysis? In 12th USENIX Workshop on Cyber Security Experimentation and Test (CSET 19).
Garcia, S., Parmisano, A., and Erquiaga, M. J. (2020). IoT-23: A labeled dataset with malicious and benign IoT network traffic. More details here [link].
Gaurav, A., Gupta, B. B., and Panigrahi, P. K. (2023). A comprehensive survey on machine learning approaches for malware detection in iot-based enterprise information system. Enterprise Information Systems, 17(3):2023764.
Goncharov, E. (2024). Ics and ot threat predictions for 2024.
Gopinath, M. and Sethuraman, S. C. (2023). A comprehensive survey on deep learning based malware detection techniques. Computer Science Review, 47:100529.
Jeelani, F., Rai, D. S., Maithani, A., and Gupta, S. (2022). The detection of iot botnet using machine learning on iot-23 dataset. In 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), volume 2, pages 634–639. IEEE.
Kolias, C., Kambourakis, G., Stavrou, A., and Voas, J. (2017). Ddos in the iot: Mirai and other botnets. Computer, 50(7):80–84.
Kumar, S. and Chandavarkar, B. (2023). Analysis of mirai malware and its components. In Machine Learning, Image Processing, Network Security and Data Sciences: Select Proceedings of 3rd International Conference on MIND 2021, pages 851–861. Springer.
Oha, C. V., Farouk, F. S., Patel, P. P., Meka, P., Nekkanti, S., Nayini, B., Carvalho, S. X., Desai, N., Patel, M., and Butakov, S. (2021). Machine learning models for malicious traffic detection in iot networks/iot-23 dataset. In International Conference on Machine Learning for Networking, pages 69–84. Springer.
Razaulla, S., Fachkha, C., Markarian, C., Gawanmeh, A., Mansoor, W., Fung, B. C., and Assi, C. (2023). The age of ransomware: A survey on the evolution, taxonomy, and research directions. IEEE Access.
Salahdine, F., Han, T., and Zhang, N. (2023). Security in 5g and beyond recent advances and future challenges. Security and Privacy, 6(1):e271.
Souza, C. H. and Arima, C. H. (2024). A hybrid approach for malware detection in sdn-enabled iot scenarios. Internet Technology Letters, page e534.
Tayyab, U.-e.-H., Khan, F. B., Durad, M. H., Khan, A., and Lee, Y. S. (2022). A survey of the recent trends in deep learning based malware detection. Journal of Cybersecurity and Privacy, 2(4):800–829.
Yang, X., Shu, L., Liu, Y., Hancke, G. P., Ferrag, M. A., and Huang, K. (2022). Physical security and safety of iot equipment: A survey of recent advances and opportunities. IEEE Transactions on Industrial Informatics, 18(7):4319–4330.
Alqudah, N. and Yaseen, Q. (2020). Machine learning for traffic analysis: a review. Procedia Computer Science, 170:911–916.
Alwashali, A. A. M. A., Abd Rahman, N. A., and Ismail, N. (2021). A survey of ransomware as a service (raas) and methods to mitigate the attack. In 2021 14th International Conference on Developments in eSystems Engineering (DeSE), pages 92–96. IEEE.
Balogh, Š., Mojžiš, J., and Krammer, P. (2022). Evaluation of system features used for malware detection. In Proceedings of the Future Technologies Conference (FTC) 2021, Volume 3, pages 46–59. Springer.
Darki, A., Faloutsos, M., Abu-Ghazaleh, N., Sridharan, M., et al. (2019). {IDAPro} for {IoT} malware analysis? In 12th USENIX Workshop on Cyber Security Experimentation and Test (CSET 19).
Garcia, S., Parmisano, A., and Erquiaga, M. J. (2020). IoT-23: A labeled dataset with malicious and benign IoT network traffic. More details here [link].
Gaurav, A., Gupta, B. B., and Panigrahi, P. K. (2023). A comprehensive survey on machine learning approaches for malware detection in iot-based enterprise information system. Enterprise Information Systems, 17(3):2023764.
Goncharov, E. (2024). Ics and ot threat predictions for 2024.
Gopinath, M. and Sethuraman, S. C. (2023). A comprehensive survey on deep learning based malware detection techniques. Computer Science Review, 47:100529.
Jeelani, F., Rai, D. S., Maithani, A., and Gupta, S. (2022). The detection of iot botnet using machine learning on iot-23 dataset. In 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), volume 2, pages 634–639. IEEE.
Kolias, C., Kambourakis, G., Stavrou, A., and Voas, J. (2017). Ddos in the iot: Mirai and other botnets. Computer, 50(7):80–84.
Kumar, S. and Chandavarkar, B. (2023). Analysis of mirai malware and its components. In Machine Learning, Image Processing, Network Security and Data Sciences: Select Proceedings of 3rd International Conference on MIND 2021, pages 851–861. Springer.
Oha, C. V., Farouk, F. S., Patel, P. P., Meka, P., Nekkanti, S., Nayini, B., Carvalho, S. X., Desai, N., Patel, M., and Butakov, S. (2021). Machine learning models for malicious traffic detection in iot networks/iot-23 dataset. In International Conference on Machine Learning for Networking, pages 69–84. Springer.
Razaulla, S., Fachkha, C., Markarian, C., Gawanmeh, A., Mansoor, W., Fung, B. C., and Assi, C. (2023). The age of ransomware: A survey on the evolution, taxonomy, and research directions. IEEE Access.
Salahdine, F., Han, T., and Zhang, N. (2023). Security in 5g and beyond recent advances and future challenges. Security and Privacy, 6(1):e271.
Souza, C. H. and Arima, C. H. (2024). A hybrid approach for malware detection in sdn-enabled iot scenarios. Internet Technology Letters, page e534.
Tayyab, U.-e.-H., Khan, F. B., Durad, M. H., Khan, A., and Lee, Y. S. (2022). A survey of the recent trends in deep learning based malware detection. Journal of Cybersecurity and Privacy, 2(4):800–829.
Yang, X., Shu, L., Liu, Y., Hancke, G. P., Ferrag, M. A., and Huang, K. (2022). Physical security and safety of iot equipment: A survey of recent advances and opportunities. IEEE Transactions on Industrial Informatics, 18(7):4319–4330.
Publicado
16/09/2024
Como Citar
SOUZA, Cristian H. M.; ARIMA, Carlos H..
Avaliação de algoritmos de machine learning para detecção de malware IoT no dataset IoT-23. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 24. , 2024, São José dos Campos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 767-772.
DOI: https://doi.org/10.5753/sbseg.2024.241472.
