Design of an Adaptive Distributed Critical-Care Extensive Response Network (AD-CERN) Using Cooperative Overlay Network
The main objective of this paper is to propose Adaptive Distributed Critical-Care Extensive Response Network (AD-CERN) which includes self-management and self-defense in the network. The proposed network has the following considerations. (1) Dynamic coevolution is elucidated with interaction between...
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| Format: | Article |
| Language: | English |
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Wiley
2014-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/754898 |
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| author | V. Akilandeswari S. Mercy Shalinie |
| author_facet | V. Akilandeswari S. Mercy Shalinie |
| author_sort | V. Akilandeswari |
| collection | DOAJ |
| description | The main objective of this paper is to propose Adaptive Distributed Critical-Care Extensive Response Network (AD-CERN) which includes self-management and self-defense in the network. The proposed network has the following considerations. (1) Dynamic coevolution is elucidated with interaction between independent rational strategies and structure of overlay network. (2) Evolutionary Game Theory (EGT) specific to overlay network is adopted with the combination of network reciprocity and group selection mechanism. (3) The cooperative efficient network structure is put forth by the multinode link-formation game. In this network, each node will be trained independently based on Naïve Bayes classification algorithm to react against any critical services in the network. The proposed network's significant characteristics are self-learning, independent training, collaborative detection, reaction to critical services, protecting the target machine before being collapsed, and maintaining the network performance without any hindrances. It controls the flow of high rate critical services such as Distributed Denial of Services (DDoS) flooding attacks and blocks the unwarranted services without any interruption to legitimate users. Hence, there is no doubt that this self-managed and self-defensive system will move from realm of fiction to real-time network engineering with high detection accuracy (98.3%), classification rate up to 99%, and improved clustering coefficient. |
| format | Article |
| id | doaj-art-462f15a1077b41cbb6b8152af6b51863 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2014-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-462f15a1077b41cbb6b8152af6b518632025-02-03T06:45:30ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-03-011010.1155/2014/754898754898Design of an Adaptive Distributed Critical-Care Extensive Response Network (AD-CERN) Using Cooperative Overlay NetworkV. AkilandeswariS. Mercy ShalinieThe main objective of this paper is to propose Adaptive Distributed Critical-Care Extensive Response Network (AD-CERN) which includes self-management and self-defense in the network. The proposed network has the following considerations. (1) Dynamic coevolution is elucidated with interaction between independent rational strategies and structure of overlay network. (2) Evolutionary Game Theory (EGT) specific to overlay network is adopted with the combination of network reciprocity and group selection mechanism. (3) The cooperative efficient network structure is put forth by the multinode link-formation game. In this network, each node will be trained independently based on Naïve Bayes classification algorithm to react against any critical services in the network. The proposed network's significant characteristics are self-learning, independent training, collaborative detection, reaction to critical services, protecting the target machine before being collapsed, and maintaining the network performance without any hindrances. It controls the flow of high rate critical services such as Distributed Denial of Services (DDoS) flooding attacks and blocks the unwarranted services without any interruption to legitimate users. Hence, there is no doubt that this self-managed and self-defensive system will move from realm of fiction to real-time network engineering with high detection accuracy (98.3%), classification rate up to 99%, and improved clustering coefficient.https://doi.org/10.1155/2014/754898 |
| spellingShingle | V. Akilandeswari S. Mercy Shalinie Design of an Adaptive Distributed Critical-Care Extensive Response Network (AD-CERN) Using Cooperative Overlay Network International Journal of Distributed Sensor Networks |
| title | Design of an Adaptive Distributed Critical-Care Extensive Response Network (AD-CERN) Using Cooperative Overlay Network |
| title_full | Design of an Adaptive Distributed Critical-Care Extensive Response Network (AD-CERN) Using Cooperative Overlay Network |
| title_fullStr | Design of an Adaptive Distributed Critical-Care Extensive Response Network (AD-CERN) Using Cooperative Overlay Network |
| title_full_unstemmed | Design of an Adaptive Distributed Critical-Care Extensive Response Network (AD-CERN) Using Cooperative Overlay Network |
| title_short | Design of an Adaptive Distributed Critical-Care Extensive Response Network (AD-CERN) Using Cooperative Overlay Network |
| title_sort | design of an adaptive distributed critical care extensive response network ad cern using cooperative overlay network |
| url | https://doi.org/10.1155/2014/754898 |
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