Comparando Seleção de Atributos na Detecção de Ataques DoS Hulk e DoS GoldenEye
Abstract
Computer networks are essential for performing a wide range of indispensable activities in daily life. However, they are subject to vulnerabilities arising from the transmission of unprotected data and the inadequate implementation of security protocols, leading to risks such as unauthorized data disclosure and interruption of critical services. In light of this reality, the present study proposes a comparative analysis of attribute selection for the detection of DoS Hulk and DoS GoldenEye attacks through a MultiClass Classifier. The goal is to determine the effectiveness of each technique in identifying these attacks, which are considered network anomalies.
References
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Dalmazo, B. L., Vilela, J. P., and Curado, M. (2018). Triple-similarity mechanism for alarm management in the cloud. Computers & Security, 78:33–42.
Jabez, J. and Muthukumar, B. (2015). Intrusion detection system (IDS): Anomaly detection using outlier detection approach. Procedia Computer Science, 48:338–346.
Kasongo, S. M. and Sun, Y. (2019). A deep learning method with filter based feature engineering for wireless intrusion detection system. IEEE Access, 7:38597–38607.
Novakovic, J., Strbac, P., and Bulatović, D. (2011). Toward optimal feature selection using ranking methods and classification algorithms. Yugoslav Journal of Operations Research, 21:119–135.
Sharafaldin, I., Lashkari, A. H., and Ghorbani, A. A. (2018). Intrusion detection evaluation dataset (cic-ids2017). Proceedings of the of Canadian Institute for Cybersecurity.
Thaseen, I. S. and Kumar, C. A. (2017). Intrusion detection model using fusion of chi-square feature selection and multi class SVM. Journal of King Saud University-Computer and Information Sciences, 29(4):462–472.
Vinayakumar, R., Alazab, M., Soman, K., Poornachandran, P., Al-Nemrat, A., and Venkatraman, S. (2019). Deep learning approach for intelligent intrusion detection system. IEEE Access, 7:41525–41550.
Published
2023-10-23
How to Cite
BARBOSA, Marcos G.; SAPATA, Matheus R.; RIKER, André; DALMAZO, Bruno L..
Comparando Seleção de Atributos na Detecção de Ataques DoS Hulk e DoS GoldenEye. In: REGIONAL SCHOOL OF COMPUTER NETWORKS (ERRC), 20. , 2023, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 157-162.
DOI: https://doi.org/10.5753/errc.2023.933.
