Um Testbed para o Ensino de Abordagens de IDS Baseadas em IA em Redes Emuladas
Resumo
Nos últimos anos, houve um aumento significativo no tráfego da internet, impulsionado pela pandemia global e pela crescente importância das atividades online. Como resultado desse crescimento, o número de crimes cibernéticos também aumentou. Nesse contexto, os Sistemas de Detecção de Intrusão (IDSs) precisam melhorar a precisão da detecção e reduzir as taxas de falsos alarmes. Este trabalho apresenta um testbed baseado em rede emulada para o ensino de abordagens IDS baseadas em Inteligência Artificial (IA). Como resultado, foi desenvolvido um ambiente de interface escalável e de alto nível, que permite à comunidade se concentrar em suas abordagens com menos preocupações com problemas relacionados ao ambiente de teste.
Referências
CICFlowMeter (2018). Applications. Disponível em: [link].
CSE-CIC (2018). CSE-CIC-IDS2018. Disponível em: [link].
Ferrag, M. A., Maglaras, L., Moschoyiannis, S., and Janicke, H. (2020). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications, 50:102419.
G. Karatas, O. D. and Koray, A. S. (2018). Deep Learning in Intrusion Detection Systems. pages 3–4. International Congress on Big Data.
Kanimozhi, V. and Jacob, T. P. (2021). Artificial intelligence outflanks all other machine learning classifiers in network intrusion detection system on the realistic cyber dataset cse-cic-ids2018 using cloud computing. ICT Express, 7(3):366–370.
Kim, A., Park, M., and Lee, D. H. (2020). Ai-ids: Application of deep learning to realtime web intrusion detection. IEEE Access, 8:70245–70261.
Mahfouz, A., Abuhussein, A., Venugopal, D., and Shiva, S. (2020). Ensemble classifiers for network intrusion detection using a novel network attack dataset. Future Internet, 12(11).
Marir, N., Wang, H., and Feng, G. (2019). Unsupervised feature learning with distributed stacked denoising sparse autoencoder for abnormal behavior detection using apache spark. In 2019 IEEE 2nd International Conference on Knowledge Innovation and Invention (ICKII), pages 473–476.
McAfee (2020). Report Cyber Crime. Disponível em: [link].
Mishra, P., Varadharajan, V., Tupakula, U., and Pilli, E. S. (2019). A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection. volume 21. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS.
N. Moustafa, J. H. and Slay, J. (2019). A holistic review of Network Anomaly Detection Systems: A comprehensive survey. volume 128, pages 33–55. Journal of Network and Computer Applications.
Sai Kiran, K., Devisetty, R. K., Kalyan, N. P., Mukundini, K., and Karthi, R. (2020). Building a intrusion detection system for iot environment using machine learning techniques. Procedia Computer Science, 171:2372–2379. Third International Conference on Computing and Network Communications (CoCoNet’19).
Silva, I., Marques, C., and Lima, R. (2017). Integrando o emulador gns3 como suporte de ensino na disciplina de redes de computadores no ambiente ava. page 1727.
Zhong, W., Yu, N., and Ai, C. (2020). Applying big data based deep learning system to intrusion detection. Big Data Mining and Analytics, 3(3):181–195.
Zieglmeier, V., Kacianka, S., Hutzelmann, T., and Pretschner, A. (2018). A real-time remote IDS testbed for connected vehicles. CoRR, abs/1811.10945.
CSE-CIC (2018). CSE-CIC-IDS2018. Disponível em: [link].
Ferrag, M. A., Maglaras, L., Moschoyiannis, S., and Janicke, H. (2020). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications, 50:102419.
G. Karatas, O. D. and Koray, A. S. (2018). Deep Learning in Intrusion Detection Systems. pages 3–4. International Congress on Big Data.
Kanimozhi, V. and Jacob, T. P. (2021). Artificial intelligence outflanks all other machine learning classifiers in network intrusion detection system on the realistic cyber dataset cse-cic-ids2018 using cloud computing. ICT Express, 7(3):366–370.
Kim, A., Park, M., and Lee, D. H. (2020). Ai-ids: Application of deep learning to realtime web intrusion detection. IEEE Access, 8:70245–70261.
Mahfouz, A., Abuhussein, A., Venugopal, D., and Shiva, S. (2020). Ensemble classifiers for network intrusion detection using a novel network attack dataset. Future Internet, 12(11).
Marir, N., Wang, H., and Feng, G. (2019). Unsupervised feature learning with distributed stacked denoising sparse autoencoder for abnormal behavior detection using apache spark. In 2019 IEEE 2nd International Conference on Knowledge Innovation and Invention (ICKII), pages 473–476.
McAfee (2020). Report Cyber Crime. Disponível em: [link].
Mishra, P., Varadharajan, V., Tupakula, U., and Pilli, E. S. (2019). A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection. volume 21. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS.
N. Moustafa, J. H. and Slay, J. (2019). A holistic review of Network Anomaly Detection Systems: A comprehensive survey. volume 128, pages 33–55. Journal of Network and Computer Applications.
Sai Kiran, K., Devisetty, R. K., Kalyan, N. P., Mukundini, K., and Karthi, R. (2020). Building a intrusion detection system for iot environment using machine learning techniques. Procedia Computer Science, 171:2372–2379. Third International Conference on Computing and Network Communications (CoCoNet’19).
Silva, I., Marques, C., and Lima, R. (2017). Integrando o emulador gns3 como suporte de ensino na disciplina de redes de computadores no ambiente ava. page 1727.
Zhong, W., Yu, N., and Ai, C. (2020). Applying big data based deep learning system to intrusion detection. Big Data Mining and Analytics, 3(3):181–195.
Zieglmeier, V., Kacianka, S., Hutzelmann, T., and Pretschner, A. (2018). A real-time remote IDS testbed for connected vehicles. CoRR, abs/1811.10945.
Publicado
23/08/2023
Como Citar
VALENTIM, Paulo E.; BRAGA, Reinaldo B.; OLIVEIRA, Antonio W..
Um Testbed para o Ensino de Abordagens de IDS Baseadas em IA em Redes Emuladas. In: CONGRESSO SOBRE TECNOLOGIAS NA EDUCAÇÃO (CTRL+E), 8. , 2023, Santarém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 292-301.
DOI: https://doi.org/10.5753/ctrle.2023.232915.
