A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks
DDoS (Distributed Denial-of-Service) attacks are among the cyberattacks that are increasing day by day and have caused problems for computer network servers. With the advent of SDN networks, they are not immune to these attacks, and due to the software-centric nature of these networks, this type of...
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| Language: | English |
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OICC Press
2024-02-01
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| Series: | Majlesi Journal of Electrical Engineering |
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| Online Access: | https://oiccpress.com/mjee/article/view/4904 |
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| author | ÙAfsaneh Banitalebi Dehkordi MohammadReza Soltanaghaei Farsad Zamani Boroujeni |
| author_facet | ÙAfsaneh Banitalebi Dehkordi MohammadReza Soltanaghaei Farsad Zamani Boroujeni |
| author_sort | ÙAfsaneh Banitalebi Dehkordi |
| collection | DOAJ |
| description | DDoS (Distributed Denial-of-Service) attacks are among the cyberattacks that are increasing day by day and have caused problems for computer network servers. With the advent of SDN networks, they are not immune to these attacks, and due to the software-centric nature of these networks, this type of attack can be much more difficult for them, ignoring effective parameters such as port and Source IP in detecting attacks, providing costly solutions which are effective in increasing CPU load, and low accuracy in detecting attacks are of the problems of previously presented methods in detecting DDoS attacks. Given the importance of this issue,the purpose of this paper is to increase the accuracy of DDoS attack detection using the second order correlation coefficient technique based on â
-entropy according to source IP and selection of optimal features.To select the best features, by examining the types of feature selection algorithms and search methods, the WrapperSubsetEval feature selection algorithm, the BestFirst search method, and the best effective features were selected. This study was performed on CTU-13 and ISOT datasets and the results were compared with other methods. The accuracy of the detection in this work indicates the high efficiency of the proposed approach compared to other similar methods. |
| format | Article |
| id | doaj-art-835aba8bcc06492abd170fa08a157765 |
| institution | OA Journals |
| issn | 2345-377X 2345-3796 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | OICC Press |
| record_format | Article |
| series | Majlesi Journal of Electrical Engineering |
| spelling | doaj-art-835aba8bcc06492abd170fa08a1577652025-08-20T02:15:54ZengOICC PressMajlesi Journal of Electrical Engineering2345-377X2345-37962024-02-0115110.52547/mjee.15.1.1A Hybrid Mechanism to Detect DDoS Attacks in Software Defined NetworksÙAfsaneh Banitalebi Dehkordi0MohammadReza Soltanaghaei1Farsad Zamani Boroujeni2Department of Computer Science,Payame Noor University(PNU),P.OBOX,19395-4697 ,Tehran,IranDepartment of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, IranDDoS (Distributed Denial-of-Service) attacks are among the cyberattacks that are increasing day by day and have caused problems for computer network servers. With the advent of SDN networks, they are not immune to these attacks, and due to the software-centric nature of these networks, this type of attack can be much more difficult for them, ignoring effective parameters such as port and Source IP in detecting attacks, providing costly solutions which are effective in increasing CPU load, and low accuracy in detecting attacks are of the problems of previously presented methods in detecting DDoS attacks. Given the importance of this issue,the purpose of this paper is to increase the accuracy of DDoS attack detection using the second order correlation coefficient technique based on â
-entropy according to source IP and selection of optimal features.To select the best features, by examining the types of feature selection algorithms and search methods, the WrapperSubsetEval feature selection algorithm, the BestFirst search method, and the best effective features were selected. This study was performed on CTU-13 and ISOT datasets and the results were compared with other methods. The accuracy of the detection in this work indicates the high efficiency of the proposed approach compared to other similar methods.https://oiccpress.com/mjee/article/view/4904â
-EntropyDDoS Attack.SDNsecond order Correlation CoefficientWrapperSubsetEval |
| spellingShingle | ÙAfsaneh Banitalebi Dehkordi MohammadReza Soltanaghaei Farsad Zamani Boroujeni A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks Majlesi Journal of Electrical Engineering â -Entropy DDoS Attack. SDN second order Correlation Coefficient WrapperSubsetEval |
| title | A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks |
| title_full | A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks |
| title_fullStr | A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks |
| title_full_unstemmed | A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks |
| title_short | A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks |
| title_sort | hybrid mechanism to detect ddos attacks in software defined networks |
| topic | â
-Entropy DDoS Attack. SDN second order Correlation Coefficient WrapperSubsetEval |
| url | https://oiccpress.com/mjee/article/view/4904 |
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