Acta Scientific Computer Sciences

Research Article Volume 5 Issue 11

Cybersecurity in Machine Learning Techniques: Detecting Network Attacks

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 21, 2023

Abstract

Using the well-known dataset HTTP DATASET CSIC 2010, this work intends to build seven machine learning methods (Decision Tree, Random Forest, Gradient Boosting, XGBoost, AdaBoost, Multilayer Perceptron, and Voting) to identify anomaly assaults. Accuracy, precision, recall, and f1-score are four common evaluation metrics used to rate the effectiveness of these models. In order to identify several attack methods on this dataset, we conducted one experiment: Binary Classification into two categories (normal and malicious attacks). The findings demonstrated that in this experiment, the voting classifier and decision tree provided the greatest performance outcomes.

Keywords: HTTP DATASET CSIC 2010; Machine Learning; Cybersecurity Attacks; Detection Attacks

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Citation

Citation: Saif Rawashdeh. “Cybersecurity in Machine Learning Techniques: Detecting Network Attacks".Acta Scientific Computer Sciences 5.11 (2023): 21-29.

Copyright

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.




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