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 preven...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Prague University of Economics and Business
2022-12-01
|
| Series: | Acta Informatica Pragensia |
| Subjects: | |
| Online Access: | https://aip.vse.cz/artkey/aip-202203-0005_efficient-machine-learning-model-for-ddos-detection-system-based-on-dimensionality-reduction.php |
| _version_ | 1826996028419604480 |
|---|---|
| author | Saad Ahmed Dheyab Shaymaa Mohammed Abdulameer Salama Mostafa |
| author_facet | Saad Ahmed Dheyab Shaymaa Mohammed Abdulameer Salama Mostafa |
| author_sort | Saad Ahmed Dheyab |
| collection | DOAJ |
| 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 | 2024-04-09T22:02:20Z |
| format | Article |
| id | doaj.art-8c9278edc08243fcb7b4877bef799193 |
| institution | Directory Open Access Journal |
| issn | 1805-4951 |
| language | English |
| last_indexed | 2025-02-18T09:40:57Z |
| publishDate | 2022-12-01 |
| publisher | Prague University of Economics and Business |
| record_format | Article |
| series | Acta Informatica Pragensia |
| spelling | doaj.art-8c9278edc08243fcb7b4877bef7991932024-11-02T16:33:07ZengPrague University of Economics and BusinessActa Informatica Pragensia1805-49512022-12-0111334836010.18267/j.aip.199aip-202203-0005Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality ReductionSaad Ahmed Dheyab0Shaymaa Mohammed Abdulameer1Salama Mostafa2College of Engineering, University of Information Technology and Communications, Baghdad, IraqCollege of Information Engineering, Al-Nahrain University, Baghdad, IraqFaculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, MalaysiaDistributed 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.https://aip.vse.cz/artkey/aip-202203-0005_efficient-machine-learning-model-for-ddos-detection-system-based-on-dimensionality-reduction.phpdistributed denial of service (ddos)intrusion detection systems (ids)machine learning (ml)random forest (rf)decision tree (dt)dimensionality reduction (dr) |
| spellingShingle | Saad Ahmed Dheyab Shaymaa Mohammed Abdulameer Salama Mostafa Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction Acta Informatica Pragensia distributed denial of service (ddos) intrusion detection systems (ids) machine learning (ml) random forest (rf) decision tree (dt) dimensionality reduction (dr) |
| 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 | distributed denial of service (ddos) intrusion detection systems (ids) machine learning (ml) random forest (rf) decision tree (dt) dimensionality reduction (dr) |
| url | https://aip.vse.cz/artkey/aip-202203-0005_efficient-machine-learning-model-for-ddos-detection-system-based-on-dimensionality-reduction.php |
| work_keys_str_mv | AT saadahmeddheyab efficientmachinelearningmodelforddosdetectionsystembasedondimensionalityreduction AT shaymaamohammedabdulameer efficientmachinelearningmodelforddosdetectionsystembasedondimensionalityreduction AT salamamostafa efficientmachinelearningmodelforddosdetectionsystembasedondimensionalityreduction |