Intelligent feature selection using particle swarm optimization algorithm with a decision tree for DDoS attack detection
The explosive development of information technology is increasingly rising cyber-attacks. Distributed denial of service (DDoS) attack is a malicious threat to the modern cyber-security world, which causes performance disruption to the network servers. It is a pernicious type of attack that can forwa...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Universitas Ahmad Dahlan
2021-03-01
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| Series: | IJAIN (International Journal of Advances in Intelligent Informatics) |
| Subjects: | |
| Online Access: | http://ijain.org/index.php/IJAIN/article/view/553 |
| _version_ | 1818609255713865728 |
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| author | Aween Abubakr Saeed Noor Ghazi Mohammed Jameel |
| author_facet | Aween Abubakr Saeed Noor Ghazi Mohammed Jameel |
| author_sort | Aween Abubakr Saeed |
| collection | DOAJ |
| description | The explosive development of information technology is increasingly rising cyber-attacks. Distributed denial of service (DDoS) attack is a malicious threat to the modern cyber-security world, which causes performance disruption to the network servers. It is a pernicious type of attack that can forward a large amount of traffic to damage one or all target’s resources simultaneously and prevents authenticated users from accessing network services. The paper aims to select the least number of relevant DDoS attack detection features by designing an intelligent wrapper feature selection model that utilizes a binary-particle swarm optimization algorithm with a decision tree classifier. In this paper, the Binary-particle swarm optimization algorithm is used to resolve discrete optimization problems such as feature selection and decision tree classifier as a performance evaluator to evaluate the wrapper model’s accuracy using the selected features from the network traffic flows. The model’s intelligence is indicated by selecting 19 convenient features out of 76 features of the dataset. The experiments were accomplished on a large DDoS dataset. The optimal selected features were evaluated with different machine learning algorithms by performance measurement metrics regarding the accuracy, Recall, Precision, and F1-score to detect DDoS attacks. The proposed model showed a high accuracy rate by decision tree classifier 99.52%, random forest 96.94%, and multi-layer perceptron 90.06 %. Also, the paper compares the outcome of the proposed model with previous feature selection models in terms of performance measurement metrics. This outcome will be useful for improving DDoS attack detection systems based on machine learning algorithms. It is also probably applied to other research topics such as DDoS attack detection in the cloud environment and DDoS attack mitigation systems. |
| first_indexed | 2024-12-16T14:55:38Z |
| format | Article |
| id | doaj.art-66a2b874e9bf4575b31298ba4d3d6eaf |
| institution | Directory Open Access Journal |
| issn | 2442-6571 2548-3161 |
| language | English |
| last_indexed | 2024-12-16T14:55:38Z |
| publishDate | 2021-03-01 |
| publisher | Universitas Ahmad Dahlan |
| record_format | Article |
| series | IJAIN (International Journal of Advances in Intelligent Informatics) |
| spelling | doaj.art-66a2b874e9bf4575b31298ba4d3d6eaf2022-12-21T22:27:28ZengUniversitas Ahmad DahlanIJAIN (International Journal of Advances in Intelligent Informatics)2442-65712548-31612021-03-0171374810.26555/ijain.v7i1.553160Intelligent feature selection using particle swarm optimization algorithm with a decision tree for DDoS attack detectionAween Abubakr Saeed0Noor Ghazi Mohammed Jameel1Sulaimani Polytechnic UniversitySulaimani Polytechnic UniversityThe explosive development of information technology is increasingly rising cyber-attacks. Distributed denial of service (DDoS) attack is a malicious threat to the modern cyber-security world, which causes performance disruption to the network servers. It is a pernicious type of attack that can forward a large amount of traffic to damage one or all target’s resources simultaneously and prevents authenticated users from accessing network services. The paper aims to select the least number of relevant DDoS attack detection features by designing an intelligent wrapper feature selection model that utilizes a binary-particle swarm optimization algorithm with a decision tree classifier. In this paper, the Binary-particle swarm optimization algorithm is used to resolve discrete optimization problems such as feature selection and decision tree classifier as a performance evaluator to evaluate the wrapper model’s accuracy using the selected features from the network traffic flows. The model’s intelligence is indicated by selecting 19 convenient features out of 76 features of the dataset. The experiments were accomplished on a large DDoS dataset. The optimal selected features were evaluated with different machine learning algorithms by performance measurement metrics regarding the accuracy, Recall, Precision, and F1-score to detect DDoS attacks. The proposed model showed a high accuracy rate by decision tree classifier 99.52%, random forest 96.94%, and multi-layer perceptron 90.06 %. Also, the paper compares the outcome of the proposed model with previous feature selection models in terms of performance measurement metrics. This outcome will be useful for improving DDoS attack detection systems based on machine learning algorithms. It is also probably applied to other research topics such as DDoS attack detection in the cloud environment and DDoS attack mitigation systems.http://ijain.org/index.php/IJAIN/article/view/553distributed denial of servicebinary particle swarm optimizationdecision tree algorithmwrapper feature selectionswarm intelligent |
| spellingShingle | Aween Abubakr Saeed Noor Ghazi Mohammed Jameel Intelligent feature selection using particle swarm optimization algorithm with a decision tree for DDoS attack detection IJAIN (International Journal of Advances in Intelligent Informatics) distributed denial of service binary particle swarm optimization decision tree algorithm wrapper feature selection swarm intelligent |
| title | Intelligent feature selection using particle swarm optimization algorithm with a decision tree for DDoS attack detection |
| title_full | Intelligent feature selection using particle swarm optimization algorithm with a decision tree for DDoS attack detection |
| title_fullStr | Intelligent feature selection using particle swarm optimization algorithm with a decision tree for DDoS attack detection |
| title_full_unstemmed | Intelligent feature selection using particle swarm optimization algorithm with a decision tree for DDoS attack detection |
| title_short | Intelligent feature selection using particle swarm optimization algorithm with a decision tree for DDoS attack detection |
| title_sort | intelligent feature selection using particle swarm optimization algorithm with a decision tree for ddos attack detection |
| topic | distributed denial of service binary particle swarm optimization decision tree algorithm wrapper feature selection swarm intelligent |
| url | http://ijain.org/index.php/IJAIN/article/view/553 |
| work_keys_str_mv | AT aweenabubakrsaeed intelligentfeatureselectionusingparticleswarmoptimizationalgorithmwithadecisiontreeforddosattackdetection AT noorghazimohammedjameel intelligentfeatureselectionusingparticleswarmoptimizationalgorithmwithadecisiontreeforddosattackdetection |