Preliminary Study for Cyber Intrusion Detection Using Machine Learning Approach

Authors

  • Amirah PT. Lentera Ilmu Publisher
  • Fitrah Karimah PT. Lentera Ilmu Publisher

DOI:

https://doi.org/10.70356/jafotik.v1i1.4

Keywords:

Information system security, Cyber attacks, Machine Learning

Abstract

This article discusses the importance of information system security in the current technological era and how the increasingly complex threat of cyber attacks demands a more sophisticated approach to detection and prevention. This initial study explores the potential of applying Machine Learning in cyber intrusion detection as a first step to developing detection systems that are adaptive and responsive to evolving threats. Through a methodology involving the collection of representative data on cyber attacks, data preparation, and Machine Learning model selection, this article describes the initial stages for understanding and testing the potential of this technology in the context of cyber security. Although it includes an example dataset, data preparation steps, and the selection of several Machine Learning algorithms, this study only gets to the model selection stage, while the model training process and performance evaluation are the focus of future work. The conclusions of this initial study emphasize the importance of selecting appropriate algorithms with specific features for effective intrusion detection against growing cyber threats.

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References

A. Sanmorino, “A study for DDOS attack classification method,” J. Phys.: Conf. Ser., vol. 1175, p. 012025, Mar. 2019, doi: 10.1088/1742-6596/1175/1/012025.

D. Kumar, R. K. Pateriya, R. K. Gupta, V. Dehalwar, and A. Sharma, “DDoS Detection using Deep Learning,” Procedia Computer Science, vol. 218, pp. 2420–2429, 2023, doi: 10.1016/j.procs.2023.01.217.

H. C. Altunay and Z. Albayrak, “A hybrid CNN + LSTM based intrusion detection system for industrial IoT networks,” Eng. Sci. Technol. an Int. J., vol. 38, p. 101322, 2023, doi: 10.1016/j.jestch.2022.101322.

F. Ullah, S. Ullah, G. Srivastava, and J. C.-W. Lin, “IDS-INT: Intrusion detection system using transformer-based transfer learning for imbalanced network traffic,” Digit. Commun. Networks, 2023, doi: 10.1016/j.dcan.2023.03.008.

M. P. Karpowicz, “Adaptive tuning of network traffic policing mechanisms for DDoS attack mitigation systems,” Eur. J. Control, vol. 61, pp. 101–118, 2021, doi: 10.1016/j.ejcon.2021.07.001.

J. F. Balarezo, S. Wang, K. G. Chavez, A. Al-Hourani, and S. Kandeepan, “A survey on DoS/DDoS attacks mathematical modelling for traditional, SDN and virtual networks,” Eng. Sci. Technol. an Int. J., vol. 31, p. 101065, 2022, doi: 10.1016/j.jestch.2021.09.011.

A. Iranmanesh and H. Reza Naji, “A protocol for cluster confirmations of SDN controllers against DDoS attacks,” Comput. Electr. Eng., vol. 93, no. June, p. 107265, 2021, doi: 10.1016/j.compeleceng.2021.107265.

P. Fränti and R. Mariescu-Istodor, “Soft precision and recall,” Pattern Recognit. Lett., vol. 167, pp. 115–121, 2023, doi: 10.1016/j.patrec.2023.02.005.

S. Choudhary and N. Kesswani, “Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 Datasets using Deep Learning in IoT,” Procedia Computer Science, vol. 167, pp. 1561–1573, 2020, doi: 10.1016/j.procs.2020.03.367.

Y. Li, E. Herrera-Viedma, G. Kou, and J. A. Morente-Molinera, “Z-number-valued rule-based decision trees,” Inf. Sci. (Ny)., vol. 643, no. May, p. 119252, 2023, doi: 10.1016/j.ins.2023.119252.

I. Zoppis, G. Mauri, and R. Dondi, Kernel methods: Support vector machines, vol. 1–3. Elsevier Ltd., 2018. doi: 10.1016/B978-0-12-809633-8.20342-7.

A. Gatera, M. Kuradusenge, G. Bajpai, C. Mikeka, and S. Shrivastava, “Comparison of random forest and support vector machine regression models for forecasting road accidents,” Sci. African, vol. 21, p. e01739, 2023, doi: 10.1016/j.sciaf.2023.e01739.

G. Stavropoulos, R. van Voorstenbosch, F.-J. van Schooten, and A. Smolinska, Random Forest and Ensemble Methods, 2nd ed. Elsevier Inc., 2020. doi: 10.1016/b978-0-12-409547-2.14589-5.

S. Belattar, O. Abdoun, and E. K. Haimoudi, “Performance analysis of the application of convolutional neural networks architectures in the agricultural diagnosis,” Indones. J. Electr. Eng. Comput. Sci., vol. 27, no. 1, pp. 156–162, 2022, doi: 10.11591/ijeecs.v27.i1.pp156-162.

Z. Sun, J. Wang, and M. Q. H. Meng, “Multi-Tree Guided Efficient Robot Motion Planning,” Procedia Comput. Sci., vol. 209, pp. 40–49, 2022, doi: 10.1016/j.procs.2022.10.096.

Published

2023-02-28

How to Cite

Amirah, & Karimah, F. (2023). Preliminary Study for Cyber Intrusion Detection Using Machine Learning Approach. Jurnal Sistem Informasi Dan Teknik Informatika (JAFOTIK), 1(1), 28–33. https://doi.org/10.70356/jafotik.v1i1.4

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