Ensemble-RNN: A Robust Framework for DDoS Detection in Cloud Environment
The advent of cloud computing has made it simpler for users to gain access to data regardless of their physical location. It works for as long as they have access to the internet through an approach where the users pay based on how they use these resources in a model referred to as âpay-as-per-usage...
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| Format: | Article |
| Language: | English |
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OICC Press
2023-12-01
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| Series: | Majlesi Journal of Electrical Engineering |
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| Online Access: | https://oiccpress.com/mjee/article/view/5030 |
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| author | Asha Varma Songa Ganesh Redy Karri |
| author_facet | Asha Varma Songa Ganesh Redy Karri |
| author_sort | Asha Varma Songa |
| collection | DOAJ |
| description | The advent of cloud computing has made it simpler for users to gain access to data regardless of their physical location. It works for as long as they have access to the internet through an approach where the users pay based on how they use these resources in a model referred to as âpay-as-per-usageâ. Despite all these advantages, cloud computing has its shortcomings. The biggest concern today is the security risks associated with the cloud. One of the biggest problems that might arise with cloud services availability is Distributed Denial of Service attacks (DDoS). DDoS attacks work by multiple machines attacking the user by sending packets with large data overhead. Therefore, the network is overwhelmed with unwanted traffic. This paper proposes an intrusion detection framework using Ensemble feature selection with RNN (ERNN) to tackle the problem at hand. It combines an Ensemble of multiple Machine Learning (ML) algorithms with a Recurrent Neural Network (RNN). The framework aims to address the issue by selecting the most relevant features using the ensemble of six ML algorithms. These selected features are then used to classify the network traffic as either normal or attack, employing RNN. The effectiveness of the proposed model is evaluated using the CICDDoS2019 dataset, which contains new types of attacks. To assess the performance of the model, metrics like precision, accuracy, F-1 score, and recall are taken into consideration. |
| format | Article |
| id | doaj-art-0155d9058d034c56be4d3e170b725d9e |
| institution | OA Journals |
| issn | 2345-377X 2345-3796 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | OICC Press |
| record_format | Article |
| series | Majlesi Journal of Electrical Engineering |
| spelling | doaj-art-0155d9058d034c56be4d3e170b725d9e2025-08-20T02:15:54ZengOICC PressMajlesi Journal of Electrical Engineering2345-377X2345-37962023-12-0117410.30486/mjee.2023.1986487.1137Ensemble-RNN: A Robust Framework for DDoS Detection in Cloud EnvironmentAsha Varma Songa0Ganesh Redy Karri1VIT-AP University, School of Computer Science and Engineering, Near Vijayawada, Andhra Pradesh, IndiaVIT-AP University, School of Computer Science and Engineering, Near Vijayawada, Andhra Pradesh, IndiaThe advent of cloud computing has made it simpler for users to gain access to data regardless of their physical location. It works for as long as they have access to the internet through an approach where the users pay based on how they use these resources in a model referred to as âpay-as-per-usageâ. Despite all these advantages, cloud computing has its shortcomings. The biggest concern today is the security risks associated with the cloud. One of the biggest problems that might arise with cloud services availability is Distributed Denial of Service attacks (DDoS). DDoS attacks work by multiple machines attacking the user by sending packets with large data overhead. Therefore, the network is overwhelmed with unwanted traffic. This paper proposes an intrusion detection framework using Ensemble feature selection with RNN (ERNN) to tackle the problem at hand. It combines an Ensemble of multiple Machine Learning (ML) algorithms with a Recurrent Neural Network (RNN). The framework aims to address the issue by selecting the most relevant features using the ensemble of six ML algorithms. These selected features are then used to classify the network traffic as either normal or attack, employing RNN. The effectiveness of the proposed model is evaluated using the CICDDoS2019 dataset, which contains new types of attacks. To assess the performance of the model, metrics like precision, accuracy, F-1 score, and recall are taken into consideration.https://oiccpress.com/mjee/article/view/5030Cloud computingDDoS attacksDeep learning techniquesMachine LearningPulse width modulation (PWM) Technique |
| spellingShingle | Asha Varma Songa Ganesh Redy Karri Ensemble-RNN: A Robust Framework for DDoS Detection in Cloud Environment Majlesi Journal of Electrical Engineering Cloud computing DDoS attacks Deep learning techniques Machine Learning Pulse width modulation (PWM) Technique |
| title | Ensemble-RNN: A Robust Framework for DDoS Detection in Cloud Environment |
| title_full | Ensemble-RNN: A Robust Framework for DDoS Detection in Cloud Environment |
| title_fullStr | Ensemble-RNN: A Robust Framework for DDoS Detection in Cloud Environment |
| title_full_unstemmed | Ensemble-RNN: A Robust Framework for DDoS Detection in Cloud Environment |
| title_short | Ensemble-RNN: A Robust Framework for DDoS Detection in Cloud Environment |
| title_sort | ensemble rnn a robust framework for ddos detection in cloud environment |
| topic | Cloud computing DDoS attacks Deep learning techniques Machine Learning Pulse width modulation (PWM) Technique |
| url | https://oiccpress.com/mjee/article/view/5030 |
| work_keys_str_mv | AT ashavarmasonga ensemblernnarobustframeworkforddosdetectionincloudenvironment AT ganeshredykarri ensemblernnarobustframeworkforddosdetectionincloudenvironment |