Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System
Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/1/140 |
| _version_ | 1797497645481066496 |
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| author | Abdulaziz Fatani Abdelghani Dahou Mohammed A. A. Al-qaness Songfeng Lu Mohamed Abd Elaziz |
| author_facet | Abdulaziz Fatani Abdelghani Dahou Mohammed A. A. Al-qaness Songfeng Lu Mohamed Abd Elaziz |
| author_sort | Abdulaziz Fatani |
| collection | DOAJ |
| description | Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators. |
| first_indexed | 2024-03-10T03:22:12Z |
| format | Article |
| id | doaj.art-87f6f36733cf4ef3862c7b7de271d7f9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:22:12Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-87f6f36733cf4ef3862c7b7de271d7f92023-11-23T12:17:30ZengMDPI AGSensors1424-82202021-12-0122114010.3390/s22010140Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection SystemAbdulaziz Fatani0Abdelghani Dahou1Mohammed A. A. Al-qaness2Songfeng Lu3Mohamed Abd Elaziz4School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaLDDI Laboratory, Faculty of Science and Technology, University of Ahmed DRAIA, Adrar 01000, AlgeriaFaculty of Engineering, Sana’a University, Sana’a 12544, YemenSchool of Cyber Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaDepartment of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, EgyptDeveloping cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.https://www.mdpi.com/1424-8220/22/1/140feature selectioncybersecuritysustainable computingintrusion detection systemAquila optimizerswarm Intelligence |
| spellingShingle | Abdulaziz Fatani Abdelghani Dahou Mohammed A. A. Al-qaness Songfeng Lu Mohamed Abd Elaziz Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System Sensors feature selection cybersecurity sustainable computing intrusion detection system Aquila optimizer swarm Intelligence |
| title | Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System |
| title_full | Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System |
| title_fullStr | Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System |
| title_full_unstemmed | Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System |
| title_short | Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System |
| title_sort | advanced feature extraction and selection approach using deep learning and aquila optimizer for iot intrusion detection system |
| topic | feature selection cybersecurity sustainable computing intrusion detection system Aquila optimizer swarm Intelligence |
| url | https://www.mdpi.com/1424-8220/22/1/140 |
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