Towards a Network Intrusion Classification System using Machine Learning

• Luna dos Santos Almeida UFSC
• Fabiola Spredemann UFSC
• Analucia Schiaffino Morales UFSC
• Roberto Rodrigues-Filho UnB
• Alison R. Panisson UFSC

DOI: https://doi.org/10.5753/eniac.2025.14253

Abstract

With the expansion of the Internet and digital transformation, cybersecurity threats have increased, including attacks such as Denial of Service (DoS), ransomware, phishing, and trojans. Intrusion Detection Systems (IDS) play a crucial role in mitigating these threats, often leveraging artificial intelligence for enhanced detection. This study proposes a machine learningbased approach for cyber-attack classification, designed for integration into IDS. Using a multilayer neural network, the model successfully classified nine attack types and normal network activities, achieving an accuracy of 90.49% through optimized intermediate layer configurations.

References

Admass, W. S., Munaye, Y. Y., and Diro, A. A. (2024). Cyber security: State of the art, challenges and future directions. Cyber Security and Applications, 2:100031.

Ali, Z., Tiberti, W., Marotta, A., and Cassioli, D. (2024). Empowering network security: Bert transformer learning approach and mlp for intrusion detection in imbalanced network traffic. IEEE Access, 12:137618–137633.

Alsmadi, M. k., Omar, K. B., Noah, S. A., and Almarashdah, I. (2009). Performance comparison of multi-layer perceptron (back propagation, delta rule and perceptron) algorithms in neural networks. In 2009 IEEE International Advance Computing Conference, pages 296–299.

Aslan, Ö., Aktuğ, S. S., Ozkan-Okay, M., Yilmaz, A. A., and Akin, E. (2023). A comprehensive review of cyber security vulnerabilities, threats, attacks, and solutions. Electronics, 12(6):1333.

Borisenko, B., Erokhin, S., Fadeev, A., and Martishin, I. (2021). Intrusion detection using multilayer perceptron and neural networks with long short-term memory. In 2021 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO), pages 1–6. IEEE.

Buczak, A. L. and Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2):1153–1176.

de Almeida Florencio, F., Moreno, E. D., Macedo, H. T., de Britto Salgueiro, R. J., do Nascimento, F. B., and Santos, F. A. O. (2018). Intrusion detection via mlp neural network using an arduino embedded system. In 2018 VIII Brazilian symposium on computing systems engineering (SBESC), pages 190–195. IEEE.

Du, D., Zhu, M., Li, X., Fei, M., Bu, S., Wu, L., and Li, K. (2023). A review on cybersecurity analysis, attack detection, and attack defense methods in cyber-physical power systems. Journal of Modern Power Systems and Clean Energy, 11(3):727–743.

Ertam, F., Kilincer, L. F., and Yaman, O. (2017). Intrusion detection in computer networks via machine learning algorithms. In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), pages 1–4.

Fathima, A., Khan, A., Uddin, M. F., Waris, M. M., Ahmad, S., Sanin, C., and Szczerbicki, E. (2023). Performance evaluation and comparative analysis of machine learning models on the unsw-nb15 dataset: a contemporary approach to cyber threat detection. Cybernetics and Systems, pages 1–17.

Golande, S. V., Vaidya, S., Pardeshi, A., Katkade, V., and Pawar, V. (2024). An efficient network intrusion detection and classification system using machine learning. learning, 4(1).

Henke, M., Costa, C., dos Santos, E. M., and Souto, E. (2011). Detecção de intrusos usando conjunto de k-nn gerado por subespaços aleatórios.

Hesham, M., Essam, M., Bahaa, M., Mohamed, A., Gomaa, M., Hany, M., and Elsersy, W. (2024). Evaluating predictive models in cybersecurity: A comparative analysis of machine and deep learning techniques for threat detection. In 2024 Intelligent Methods, Systems, and Applications (IMSA), pages 33–38. IEEE.

Jang-Jaccard, J. and Nepal, S. (2014). A survey of emerging threats in cybersecurity. Journal of computer and system sciences, 80(5):973–993.

Kumar, A. and Gutierrez, J. A. (2025). Impact of machine learning on intrusion detection systems for the protection of critical infrastructure. Information, 16(7).

Liao, H.-J., Lin, C.-H. R., Lin, Y.-C., and Tung, K.-Y. (2013). Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications, 36(1):16–24.

Makhdoomi, P. M. S., Ikhlas, M., Khursheed, A., Hilal, F., Ahmad Najar, Z., Hameed, J., and Sharma, S. (2025). Network-based intrusion detection: a comparative analysis of machine learning approaches for improved security. Journal of Cyber Security Technology, pages 1–28.

Mukkamala, S., Janoski, G., and Sung, A. (2002). Intrusion detection using neural networks and support vector machines. In Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No.02CH37290), volume 2, pages 1702–1707 vol.2.

Neto, M. S. and Gomes, D. G. (2019). Network intrusion detection systems design: A machine learning approach. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 932–945, Porto Alegre, RS, Brasil. SBC.

Palenzuela, F., Shaffer, M., Ennis, M., Gorski, J., McGrew, D., Yowler, D., White, D., Holbrook, L., Yakopcic, C., and Taha, T. M. (2016). Multilayer perceptron algorithms for cyberattack detection. In 2016 IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS), pages 248–252. IEEE.

Ramos, A. and Santos, C. (2011). Combinando algoritmos de classificação para detecção de intrusão em redes de computadores. In Anais do XI Simpósio Brasileiro em Segurança da Informação e de Sistemas Computacionais, pages 211–224, Porto Alegre, RS, Brasil. SBC.

Russell, S. J. and Norvig, P. (2016). Artificial intelligence: a modern approach. pearson.

Sabahi, F. and Movaghar, A. (2008). Intrusion detection: A survey. In 2008 Third International Conference on Systems and Networks Communications, pages 23–26. IEEE.

Saheed, Y. K., Abiodun, A. I., Misra, S., Holone, M. K., and Colomo-Palacios, R. (2022). A machine learning-based intrusion detection for detecting internet of things network attacks. Alexandria Engineering Journal, 61(12):9395–9409.

Wollowski, M., Selkowitz, R., Brown, L., Goel, A., Luger, G., Marshall, J., Neel, A., Neller, T., and Norvig, P. (2016). A survey of current practice and teaching of ai. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 30.