Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Distributed denial of service (DDoS) attacks are one of the most common global challenges faced by service providers on the web. It leads to network disturbances, interruption of communication and significant damage to services. Researchers seek to develop intelligent algorithms to detect and preve...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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2022
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| Online Access: | http://eprints.uthm.edu.my/8573/1/J15756_c26c2f982c362fc78626f1ce3661d148.pdf |
| _version_ | 1825710598742081536 |
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| author | Dheyab, Saad Ahmed Mohammed Abdulameer, Shaymaa Mostafa, Salama A |
| author_facet | Dheyab, Saad Ahmed Mohammed Abdulameer, Shaymaa Mostafa, Salama A |
| author_sort | Dheyab, Saad Ahmed |
| collection | UTHM |
| description | Distributed denial of service (DDoS) attacks are one of the most common global challenges faced by service providers on the web. It leads to network disturbances, interruption of communication and
significant damage to services. Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. The present study proposes an efficient DDoS attack detection model. This model relies mainly on dimensionality reduction and machine learning algorithms. The principal component analysis (PCA) and the linear discriminant analysis (LDA) techniques perform the dimensionality reduction in individual and hybrid modes to process and improve the data. Subsequently, DDoS attack detection is performed based on random forest (RF) and decision tree (DT) algorithms. The model is implemented and tested on the CICDDoS2019 dataset using different data dimensionality reduction
test scenarios. The results show that using dimensionality reduction techniques along with the ML algorithms with a dataset containing high-dimensional data significantly improves the classification
results. The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40. |
| first_indexed | 2024-03-05T22:00:02Z |
| format | Article |
| id | uthm.eprints-8573 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:00:02Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | uthm.eprints-85732023-04-11T03:23:03Z http://eprints.uthm.edu.my/8573/ Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction Dheyab, Saad Ahmed Mohammed Abdulameer, Shaymaa Mostafa, Salama A TJ212-225 Control engineering systems. Automatic machinery (General) Distributed denial of service (DDoS) attacks are one of the most common global challenges faced by service providers on the web. It leads to network disturbances, interruption of communication and significant damage to services. Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. The present study proposes an efficient DDoS attack detection model. This model relies mainly on dimensionality reduction and machine learning algorithms. The principal component analysis (PCA) and the linear discriminant analysis (LDA) techniques perform the dimensionality reduction in individual and hybrid modes to process and improve the data. Subsequently, DDoS attack detection is performed based on random forest (RF) and decision tree (DT) algorithms. The model is implemented and tested on the CICDDoS2019 dataset using different data dimensionality reduction test scenarios. The results show that using dimensionality reduction techniques along with the ML algorithms with a dataset containing high-dimensional data significantly improves the classification results. The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40. 2022 Article PeerReviewed text en http://eprints.uthm.edu.my/8573/1/J15756_c26c2f982c362fc78626f1ce3661d148.pdf Dheyab, Saad Ahmed and Mohammed Abdulameer, Shaymaa and Mostafa, Salama A (2022) Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction. Acta Informatica Pragensia, 11 (3). pp. 1-13. https://doi.org/10.18267/j.aip.199 |
| spellingShingle | TJ212-225 Control engineering systems. Automatic machinery (General) Dheyab, Saad Ahmed Mohammed Abdulameer, Shaymaa Mostafa, Salama A Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction |
| title | Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction |
| title_full | Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction |
| title_fullStr | Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction |
| title_full_unstemmed | Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction |
| title_short | Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction |
| title_sort | efficient machine learning model for ddos detection system based on dimensionality reduction |
| topic | TJ212-225 Control engineering systems. Automatic machinery (General) |
| url | http://eprints.uthm.edu.my/8573/1/J15756_c26c2f982c362fc78626f1ce3661d148.pdf |
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