Acta Scientific Computer Sciences

Research Article Volume 5 Issue 11

Detecting Cybersecurity Attacks Using Machine Learning Techniques

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 20, 2023


The goal of this study is to detect anomaly assaults using a variety of machine learning methods (Decision Tree, Random Forest, Gradient Boosting, XGBoost, AdaBoost, Multilayer Perceptron, and Voting) using the well-known dataset NSL-KDD. Accuracy, precision, recall, and f1-score are the four assessment measures used to evaluate the performance of these algorithms. As a result, we will run two experiments to look for different kinds of assaults on this dataset: 1) Two categories of binary classification (normal and malicious attacks). 2) Multiclass classification (malicious attacks types). These tests check if the algorithms can distinguish between the many types of harmful attacks that can be found in the NSL-KDD dataset. The outcomes demonstrated that in both studies, the XGB classifier had the greatest performance results.

Keywords: NSL-KDD Dataset; Machine Learning; Cybersecurity Attacks; Detection Attacks


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Citation: Saif Rawashdeh. “Detecting Cybersecurity Attacks Using Machine Learning Techniques".Acta Scientific Computer Sciences 5.11 (2023): 11-20.


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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