Filter-Based Ensemble Feature Selection and Deep Learning Model for Intrusion Detection in Cloud Computing
In recent years, the high improvement in communication, Internet of Things (IoT) and cloud computing have begun complex questioning in security. Based on the development, cyberattacks can be increased since the present security techniques do not give optimal solutions. As a result, the authors of th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/3/556 |
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| author | C. Kavitha Saravanan M. Thippa Reddy Gadekallu Nimala K. Balasubramanian Prabhu Kavin Wen-Cheng Lai |
| author_facet | C. Kavitha Saravanan M. Thippa Reddy Gadekallu Nimala K. Balasubramanian Prabhu Kavin Wen-Cheng Lai |
| author_sort | C. Kavitha |
| collection | DOAJ |
| description | In recent years, the high improvement in communication, Internet of Things (IoT) and cloud computing have begun complex questioning in security. Based on the development, cyberattacks can be increased since the present security techniques do not give optimal solutions. As a result, the authors of this paper created filter-based ensemble feature selection (FEFS) and employed a deep learning model (DLM) for cloud computing intrusion detection. Initially, the intrusion data were collected from the global datasets of KDDCup-99 and NSL-KDD. The data were utilized for validation of the proposed methodology. The collected database was utilized for feature selection to empower the intrusion prediction. The FEFS is a combination of three feature extraction processes: filter, wrapper and embedded algorithms. Based on the above feature extraction process, the essential features were selected for enabling the training process in the DLM. Finally, the classifier received the chosen features. The DLM is a combination of a recurrent neural network (RNN) and Tasmanian devil optimization (TDO). In the RNN, the optimal weighting parameter is selected with the assistance of the TDO. The proposed technique was implemented in MATLAB, and its effectiveness was assessed using performance metrics including sensitivity, F measure, precision, sensitivity, recall and accuracy. The proposed method was compared with the conventional techniques such as an RNN and deep neural network (DNN) and RNN–genetic algorithm (RNN-GA), respectively. |
| first_indexed | 2024-03-11T09:47:38Z |
| format | Article |
| id | doaj.art-0f0fd5eff5ee47b48af23bd04ea2c1db |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T09:47:38Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-0f0fd5eff5ee47b48af23bd04ea2c1db2023-11-16T16:28:09ZengMDPI AGElectronics2079-92922023-01-0112355610.3390/electronics12030556Filter-Based Ensemble Feature Selection and Deep Learning Model for Intrusion Detection in Cloud ComputingC. Kavitha0Saravanan M.1Thippa Reddy Gadekallu2Nimala K.3Balasubramanian Prabhu Kavin4Wen-Cheng Lai5Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600083, Tamil Nadu, IndiaDepartment of Networking and Communications, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur 603203, Tamil Nadu, IndiaSchool of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, IndiaDepartment of Networking and Communications, School of Computing, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur 603203, Tamil Nadu, IndiaDepartment of Data Science and Business System, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur 603203, Tamil Nadu, IndiaBachelor Program in Industrial Projects, National Yunlin University of Science and Technology, Douliu 640301, TaiwanIn recent years, the high improvement in communication, Internet of Things (IoT) and cloud computing have begun complex questioning in security. Based on the development, cyberattacks can be increased since the present security techniques do not give optimal solutions. As a result, the authors of this paper created filter-based ensemble feature selection (FEFS) and employed a deep learning model (DLM) for cloud computing intrusion detection. Initially, the intrusion data were collected from the global datasets of KDDCup-99 and NSL-KDD. The data were utilized for validation of the proposed methodology. The collected database was utilized for feature selection to empower the intrusion prediction. The FEFS is a combination of three feature extraction processes: filter, wrapper and embedded algorithms. Based on the above feature extraction process, the essential features were selected for enabling the training process in the DLM. Finally, the classifier received the chosen features. The DLM is a combination of a recurrent neural network (RNN) and Tasmanian devil optimization (TDO). In the RNN, the optimal weighting parameter is selected with the assistance of the TDO. The proposed technique was implemented in MATLAB, and its effectiveness was assessed using performance metrics including sensitivity, F measure, precision, sensitivity, recall and accuracy. The proposed method was compared with the conventional techniques such as an RNN and deep neural network (DNN) and RNN–genetic algorithm (RNN-GA), respectively.https://www.mdpi.com/2079-9292/12/3/556intrusion detectionrecurrent neural networkdeep learning modelfilter-based ensemble feature selectioncloud computing |
| spellingShingle | C. Kavitha Saravanan M. Thippa Reddy Gadekallu Nimala K. Balasubramanian Prabhu Kavin Wen-Cheng Lai Filter-Based Ensemble Feature Selection and Deep Learning Model for Intrusion Detection in Cloud Computing Electronics intrusion detection recurrent neural network deep learning model filter-based ensemble feature selection cloud computing |
| title | Filter-Based Ensemble Feature Selection and Deep Learning Model for Intrusion Detection in Cloud Computing |
| title_full | Filter-Based Ensemble Feature Selection and Deep Learning Model for Intrusion Detection in Cloud Computing |
| title_fullStr | Filter-Based Ensemble Feature Selection and Deep Learning Model for Intrusion Detection in Cloud Computing |
| title_full_unstemmed | Filter-Based Ensemble Feature Selection and Deep Learning Model for Intrusion Detection in Cloud Computing |
| title_short | Filter-Based Ensemble Feature Selection and Deep Learning Model for Intrusion Detection in Cloud Computing |
| title_sort | filter based ensemble feature selection and deep learning model for intrusion detection in cloud computing |
| topic | intrusion detection recurrent neural network deep learning model filter-based ensemble feature selection cloud computing |
| url | https://www.mdpi.com/2079-9292/12/3/556 |
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