Evaluation of boruta algorithm in DDoS detection
Distributed Denial of Service (DDoS) is a type of attack that leverages many compromised systems or computers, as well as multiple Internet connections, to flood targeted resources simultaneously. A DDoS attack's main purpose is to disrupt website traffic and cause it to crash. As traffic grows...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/37625/ http://umpir.ump.edu.my/id/eprint/37625/1/Evaluation%20of%20Boruta%20algorithm%20in%20DDoS%20detection.pdf |
| _version_ | 1848825301621014528 |
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| author | Noor Farhana, Mohd Zuki Ahmad Firdaus, Zainal Abidin Mohd Faaizie, Darmawan Mohd Faizal, Ab Razak |
| author_facet | Noor Farhana, Mohd Zuki Ahmad Firdaus, Zainal Abidin Mohd Faaizie, Darmawan Mohd Faizal, Ab Razak |
| author_sort | Noor Farhana, Mohd Zuki |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Distributed Denial of Service (DDoS) is a type of attack that leverages many compromised systems or computers, as well as multiple Internet connections, to flood targeted resources simultaneously. A DDoS attack's main purpose is to disrupt website traffic and cause it to crash. As traffic grows over time, detecting a Distributed Denial of Service (DDoS) assault is a challenging task. Furthermore, a dataset containing a large number of features may degrade machine learning's detection performance. Therefore, in machine learning, it is necessary to prepare a relevant list of features for the training phase in order to obtain good accuracy performance. With far too many possibilities, choosing the relevant feature is complicated. This study proposes the Boruta algorithm as a suitable approach to achieve accuracy in identifying the relevant features. To evaluate the Boruta algorithm, multiple classifiers (J48, random forest, naïve bayes, and multilayer perceptron) were used so as to determine the effectiveness of the features selected by the the Boruta algorithm. The outcomes obtained showed that the random forest classifier had a higher value, with a 100% true positive rate, and 99.993% in the performance measure of accuracy, when compared to other classifiers. |
| first_indexed | 2025-11-15T03:26:45Z |
| format | Article |
| id | ump-37625 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:26:45Z |
| publishDate | 2023 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-376252023-08-29T07:43:12Z http://umpir.ump.edu.my/id/eprint/37625/ Evaluation of boruta algorithm in DDoS detection Noor Farhana, Mohd Zuki Ahmad Firdaus, Zainal Abidin Mohd Faaizie, Darmawan Mohd Faizal, Ab Razak QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Distributed Denial of Service (DDoS) is a type of attack that leverages many compromised systems or computers, as well as multiple Internet connections, to flood targeted resources simultaneously. A DDoS attack's main purpose is to disrupt website traffic and cause it to crash. As traffic grows over time, detecting a Distributed Denial of Service (DDoS) assault is a challenging task. Furthermore, a dataset containing a large number of features may degrade machine learning's detection performance. Therefore, in machine learning, it is necessary to prepare a relevant list of features for the training phase in order to obtain good accuracy performance. With far too many possibilities, choosing the relevant feature is complicated. This study proposes the Boruta algorithm as a suitable approach to achieve accuracy in identifying the relevant features. To evaluate the Boruta algorithm, multiple classifiers (J48, random forest, naïve bayes, and multilayer perceptron) were used so as to determine the effectiveness of the features selected by the the Boruta algorithm. The outcomes obtained showed that the random forest classifier had a higher value, with a 100% true positive rate, and 99.993% in the performance measure of accuracy, when compared to other classifiers. Elsevier 2023-03 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/37625/1/Evaluation%20of%20Boruta%20algorithm%20in%20DDoS%20detection.pdf Noor Farhana, Mohd Zuki and Ahmad Firdaus, Zainal Abidin and Mohd Faaizie, Darmawan and Mohd Faizal, Ab Razak (2023) Evaluation of boruta algorithm in DDoS detection. Egyptian Informatics Journal, 24 (1). pp. 27-42. ISSN 1110-8665. (Published) https://doi.org/10.1016/j.eij.2022.10.005 https://doi.org/10.1016/j.eij.2022.10.005 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Noor Farhana, Mohd Zuki Ahmad Firdaus, Zainal Abidin Mohd Faaizie, Darmawan Mohd Faizal, Ab Razak Evaluation of boruta algorithm in DDoS detection |
| title | Evaluation of boruta algorithm in DDoS detection |
| title_full | Evaluation of boruta algorithm in DDoS detection |
| title_fullStr | Evaluation of boruta algorithm in DDoS detection |
| title_full_unstemmed | Evaluation of boruta algorithm in DDoS detection |
| title_short | Evaluation of boruta algorithm in DDoS detection |
| title_sort | evaluation of boruta algorithm in ddos detection |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/37625/ http://umpir.ump.edu.my/id/eprint/37625/ http://umpir.ump.edu.my/id/eprint/37625/ http://umpir.ump.edu.my/id/eprint/37625/1/Evaluation%20of%20Boruta%20algorithm%20in%20DDoS%20detection.pdf |