Detection of DDoS Attacks in Software Defined Networking Using Entropy
Software Defined Networking (SDN) is one of the most commonly used network architectures in recent years. With the substantial increase in the number of Internet users, network security threats appear more frequently, which brings more concerns to SDN. Distributed denial of Service (DDoS) attacks ar...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/1/370 |
| _version_ | 1797499608873566208 |
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| author | Cong Fan Nitheesh Murugan Kaliyamurthy Shi Chen He Jiang Yiwen Zhou Carlene Campbell |
| author_facet | Cong Fan Nitheesh Murugan Kaliyamurthy Shi Chen He Jiang Yiwen Zhou Carlene Campbell |
| author_sort | Cong Fan |
| collection | DOAJ |
| description | Software Defined Networking (SDN) is one of the most commonly used network architectures in recent years. With the substantial increase in the number of Internet users, network security threats appear more frequently, which brings more concerns to SDN. Distributed denial of Service (DDoS) attacks are one of the most dangerous and frequent attacks in software defined networks. The traditional attack detection method using entropy has some defects such as slow attack detection and poor detection effect. In order to solve this problem, this paper proposed a method of fusion entropy, which detects attacks by measuring the randomness of network events. This method has the advantages of fast attack detection speed and obvious decrease in entropy value. The complementarity of information entropy and log energy entropy is effectively utilized. The experimental results show that the entropy value of the attack scenarios 91.25% lower than normal scenarios, which has greater advantages and significance compared with other attack detection methods. |
| first_indexed | 2024-03-10T03:49:51Z |
| format | Article |
| id | doaj.art-fb7fbffa6897446c87a4390311f26f09 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:49:51Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-fb7fbffa6897446c87a4390311f26f092023-11-23T11:11:56ZengMDPI AGApplied Sciences2076-34172021-12-0112137010.3390/app12010370Detection of DDoS Attacks in Software Defined Networking Using EntropyCong Fan0Nitheesh Murugan Kaliyamurthy1Shi Chen2He Jiang3Yiwen Zhou4Carlene Campbell5School of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaWales Institute of Science and Art, University of Wales Trinity Saint David, Swansea SA1 8PH, UKSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaWales Institute of Science and Art, University of Wales Trinity Saint David, Swansea SA1 8PH, UKSoftware Defined Networking (SDN) is one of the most commonly used network architectures in recent years. With the substantial increase in the number of Internet users, network security threats appear more frequently, which brings more concerns to SDN. Distributed denial of Service (DDoS) attacks are one of the most dangerous and frequent attacks in software defined networks. The traditional attack detection method using entropy has some defects such as slow attack detection and poor detection effect. In order to solve this problem, this paper proposed a method of fusion entropy, which detects attacks by measuring the randomness of network events. This method has the advantages of fast attack detection speed and obvious decrease in entropy value. The complementarity of information entropy and log energy entropy is effectively utilized. The experimental results show that the entropy value of the attack scenarios 91.25% lower than normal scenarios, which has greater advantages and significance compared with other attack detection methods.https://www.mdpi.com/2076-3417/12/1/370software defined networkingentropydistributed denial of service attacks |
| spellingShingle | Cong Fan Nitheesh Murugan Kaliyamurthy Shi Chen He Jiang Yiwen Zhou Carlene Campbell Detection of DDoS Attacks in Software Defined Networking Using Entropy Applied Sciences software defined networking entropy distributed denial of service attacks |
| title | Detection of DDoS Attacks in Software Defined Networking Using Entropy |
| title_full | Detection of DDoS Attacks in Software Defined Networking Using Entropy |
| title_fullStr | Detection of DDoS Attacks in Software Defined Networking Using Entropy |
| title_full_unstemmed | Detection of DDoS Attacks in Software Defined Networking Using Entropy |
| title_short | Detection of DDoS Attacks in Software Defined Networking Using Entropy |
| title_sort | detection of ddos attacks in software defined networking using entropy |
| topic | software defined networking entropy distributed denial of service attacks |
| url | https://www.mdpi.com/2076-3417/12/1/370 |
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