Acta Scientific Computer Sciences

Research Article Volume 5 Issue 11

Applying Machine Learning Techniques in Cybersecurity Field

Saif Rawashdeh*

Department of Computer Science, Jordan University of Science and Technology, Jordan

*Corresponding Author: Saif Rawashdeh, Department of Computer Science, Jordan University of Science and Technology, Jordan.

Received: October 11, 2023; Published: October 22, 2023


Machine learning techniques have been applied in various fields and shown to be effective, like cybersecurity. Machine learning can be used in cybersecurity to detect and defend against network attacks. It can also be used to detect anomalies in system behavior that may indicate an attack is underway. Machine learning is a valuable tool for cybersecurity professionals and can help make systems more secure. This paper aims to develop seven machine learning algorithms (Decision Tree, Random Forest, Gradient Boosting, XGBoost, AdaBoost, Multilayer Perceptron, and Voting) to detect anomaly attacks using a well-known dataset named UNSW-NB15. To assess the performance of these models, there are four popular evaluation metrics: accuracy, precision, recall, and f1-score. Therefore, we applied two experiments and an enchantment experiment to detect several types of attacks: 1) Binary classification into two types of attacks (normal and malicious). 2) Multiclass classification (types of malicious attacks). 3) Enchantment experiment on the second experiment (choose the three most frequent attacks in the dataset out of nine attacks). These experiments are done to see if each algorithm is able to distinguish between the types of malicious attacks in the UNSW_NB15 dataset. The results showed that the voting classifier performed the best in the first experiment. Furthermore, when compared to others, the XGB performed better in the second and enchantment experiments.

Keywords: UNSW_NB15 Dataset; Machine Learning; Cybersecurity Attacks; Detection Attacks


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Citation: Saif Rawashdeh. “Applying Machine Learning Techniques in Cybersecurity Field".Acta Scientific Computer Sciences 5.11 (2023): 30-39.


Copyright: © 2023 Saif Rawashdeh. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Acceptance rate35%
Acceptance to publication20-30 days

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