Evaluating MLP and Autoencoder Models for Zero-Day Attack Detection in 6G Networks
Abstract
Sixth Generation (6G) networks promise a deep integration with Artificial Intelligence (AI), enabling intelligent and efficient communication systems. However, this evolution also introduces new cybersecurity threats, including Zero-Day attacks that exploit previously unknown system vulnerabilities. This paper evaluates two widely adopted models, the Multilayer Perceptron (MLP) and the Autoencoder, for attack detection, with a particular focus on Zero-Day attacks. Using a dataset collected from a real 5G network, the study evaluates each model not only in terms of detection performance but also with respect to computational resource consumption. The results indicate that the MLP model outperforms the Autoencoder in both overall classification accuracy and zeroday attack detection, albeit at the cost of higher computational resource usage.
References
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Abri, F., Siami-Namini, S., Khanghah, M. A., Soltani, F. M., and Namin, A. S. (2019). Can machine/deep learning classifiers detect zero-day malware with high accuracy? In 2019 IEEE international conference on big data (Big Data), pages 3252–3259. IEEE.
Agbedanu, P. R., Yang, S. J., Musabe, R., Gatare, I., and Rwigema, J. (2025). A scalable approach to internet of things and industrial internet of things security: Evaluating adaptive self-adjusting memory k-nearest neighbor for zero-day attack detection. Sensors, 25(1):216.
Ahmad, R., Alsmadi, I., Alhamdani, W., and Tawalbeh, L. (2023). Zero-day attack detection: a systematic literature review. Artificial Intelligence Review, 56(10):10733–10811.
Al-Rushdan, H., Shurman, M., Alnabelsi, S. H., and Althebyan, Q. (2019). Zero-day attack detection and prevention in software-defined networks. In 2019 International Arab Conference on Information Technology (ACIT), pages 278–282.
Ali, S., Rehman, S. U., Imran, A., Adeem, G., Iqbal, Z., and Kim, K.-I. (2022). Comparative evaluation of ai-based techniques for zero-day attacks detection. Electronics, 11(23):3934.
Benzaïd, C., Hossain, F. M., Taleb, T., Gómez, P. M., and Dieudonne, M. (2024). A federated continual learning framework for sustainable network anomaly detection in o-ran. In 2024 IEEE Wireless Communications and Networking Conference (WCNC), pages 1–6. IEEE.
Chollet, F. (2015). Keras. GitHub repository: [link].
De Azambuja, A. J. G., Plesker, C., Schützer, K., Anderl, R., Schleich, B., and Almeida, V. R. (2023). Artificial intelligence-based cyber security in the context of industry 4.0—a survey. Electronics, 12(8):1920.
Foundation, P. S. (2025). Python documentation. [link].
Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep learning, volume 1. MIT press Cambridge.
Google (2025). Google colaboratory. [link].
Guo, Y. (2023). A review of machine learning-based zero-day attack detection: Challenges and future directions. Computer communications, 198:175–185.
Hamid, K., Iqbal, M. W., Aqeel, M., Liu, X., and Arif, M. (2022). Analysis of techniques for detection and removal of zero-day attacks (zda). In International Conference on Ubiquitous Security, pages 248–262. Springer.
Hindy, H., Atkinson, R., Tachtatzis, C., Colin, J.-N., Bayne, E., and Bellekens, X. (2020). Utilising deep learning techniques for effective zero-day attack detection. Electronics, 9(10):1684.
Imoize, A. L., Adedeji, O., Tandiya, N., and Shetty, S. (2021). 6G enabled smart infrastructure for sustainable society: Opportunities, challenges, and research roadmap. Sensors, 21(5).
Jain, P., Gupta, A., and Kumar, N. (2022). A vision towards integrated 6G communication networks: Promising technologies, architecture, and use-cases. Physical Communication, 55:101917.
Kianpisheh, S. and Taleb, T. (2024). Collaborative federated learning for 6G with a deep reinforcement learning based controlling mechanism: A ddos attack detection scenario. IEEE Transactions on Network and Service Management.
Lawrence, S. and Giles, C. L. (2000). Overfitting and neural networks: conjugate gradient and backpropagation. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, volume 1, pages 114–119. IEEE.
Lu, H., Zhao, Y., Song, Y., Yang, Y., He, G., Yu, H., and Ren, Y. (2024). A transfer learning-based intrusion detection system for zero-day attack in communication-based train control system. Cluster Computing, 27(6):8477–8492.
Mohamed, A. A., Al-Saleh, A., Sharma, S. K., and Tejani, G. G. (2025). Zero-day exploits detection with adaptive wavepca-autoencoder (awpa) adaptive hybrid exploit detection network (ahednet). Scientific Reports, 15(1):4036.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 32.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12:2825–2830.
Redino, C., Nandakumar, D., Schiller, R., Choi, K., Rahman, A., Bowen, E., Shaha, A., Nehila, J., and Weeks, M. (2022). Zero day threat detection using graph and flow based security telemetry. In 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pages 655–662. IEEE.
Samarakoon, S., Siriwardhana, Y., Porambage, P., Liyanage, M., Chang, S.-Y., Kim, J., Kim, J., and Ylianttila, M. (2022). 5G-NIDD: A comprehensive network intrusion detection dataset generated over 5G wireless network. arXiv preprint arXiv:2212.01298.
Sameera, N. and Shashi, M. (2020). Deep transductive transfer learning framework for zero-day attack detection. ICT Express, 6(4):361–367.
Sarhan, M., Layeghy, S., Gallagher, M., and Portmann, M. (2023). From zero-shot machine learning to zero-day attack detection. International Journal of Information Security, 22(4):947–959.
Taud, H. and Mas, J.-F. (2018). Multilayer perceptron (MLP). Geomatic approaches for modeling land change scenarios, pages 451–455.
Team, J. D. (2025). Jupyter documentation. [link].
Teoh, T., Chiew, G., Franco, E. J., Ng, P., Benjamin, M., and Goh, Y. (2018). Anomaly detection in cyber security attacks on networks using MLP deep learning. In 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), pages 1–5. IEEE.
The pandas development team (2024). pandas-dev/pandas: Pandas.
Wu, J., Li, R., An, X., Peng, C., Liu, Z., Crowcroft, J., and Zhang, H. (2021). Toward native artificial intelligence in 6G networks: System design, architectures, and paradigms. arXiv preprint arXiv:2103.02823.
Yoshioka, P., Damasceno, M., Balieiro, A., Swain, S. N., and Alves, E. (2024). A decision-tree solution for the outdated CQI feedback problem in 5G networks. In 2024 XIV Brazilian Symposium on Computing Systems Engineering (SBESC), pages 1–6. IEEE.
Zhang, Z., Cao, Y., Cui, Z., Zhang, W., and Chen, J. (2021). A many-objective optimization based intelligent intrusion detection algorithm for enhancing security of vehicular networks in 6G. IEEE Transactions on Vehicular Technology, 70(6):5234–5243.
Ziegler, V., Viswanathan, H., Flinck, H., Hoffmann, M., Räisänen, V., and Hätönen, K. (2020). 6G architecture to connect the worlds. IEEE Access, 8:173508–173520.
Published
2025-09-01
How to Cite
DAMASCENO, Maria Gabriela Lima; SOUZA, Caio Bruno Bezerra de; BALIEIRO, Andson Marreiros.
Evaluating MLP and Autoencoder Models for Zero-Day Attack Detection in 6G Networks. In: BRAZILIAN SYMPOSIUM ON CYBERSECURITY (SBSEG), 25. , 2025, Foz do Iguaçu/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 384-400.
DOI: https://doi.org/10.5753/sbseg.2025.11480.
