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...

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Vydáno v:	Sensors (Basel, Switzerland) Ročník 22; číslo 1; s. 140
Hlavní autoři:	Fatani, Abdulaziz, Dahou, Abdelghani, Al-qaness, Mohammed A. A., Lu, Songfeng, Abd Elaziz, Mohamed Abd
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 26.12.2021 MDPI
Témata:	Accuracy Aquila optimizer Classification Cybersecurity Datasets Deep learning Exploitation Feature selection Internet of Things intrusion detection system Intrusion detection systems Machine learning Neural networks Optimization algorithms Optimization techniques Support vector machines sustainable computing swarm Intelligence intrusion detection system swarm Intelligence internet of things (IoT) Aquila optimizer sustainable computing cybersecurity feature selection
ISSN:	1424-8220, 1424-8220
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Abstract	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.
AbstractList	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. 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.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.
Author	Fatani, Abdulaziz Al-qaness, Mohammed A. A. Dahou, Abdelghani Abd Elaziz, Mohamed Abd Lu, Songfeng
AuthorAffiliation	2 Computer Science Department, Umm Al-Qura University, Makkah 24381, Saudi Arabia 7 Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518057, China 10 Faculty of Computer Science & Engineering, Galala University, Suze 435611, Egypt 5 State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China 3 LDDI Laboratory, Faculty of Science and Technology, University of Ahmed DRAIA, Adrar 01000, Algeria; dahou.abdghani@univ-adrar.edu.dz 4 Faculty of Engineering, Sana’a University, Sana’a 12544, Yemen 8 Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt; abd_el_aziz_m@yahoo.com 6 School of Cyber Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China 9 Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates 1 School of Computer Science and Technology, Huazhong University of Science and Technolo
AuthorAffiliation_xml	– name: 1 School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; aafatani@uqu.edu.sa – name: 5 State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China – name: 9 Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates – name: 6 School of Cyber Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China – name: 10 Faculty of Computer Science & Engineering, Galala University, Suze 435611, Egypt – name: 2 Computer Science Department, Umm Al-Qura University, Makkah 24381, Saudi Arabia – name: 8 Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt; abd_el_aziz_m@yahoo.com – name: 3 LDDI Laboratory, Faculty of Science and Technology, University of Ahmed DRAIA, Adrar 01000, Algeria; dahou.abdghani@univ-adrar.edu.dz – name: 4 Faculty of Engineering, Sana’a University, Sana’a 12544, Yemen – name: 7 Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518057, China
Author_xml	– sequence: 1 givenname: Abdulaziz orcidid: 0000-0002-9097-0945 surname: Fatani fullname: Fatani, Abdulaziz – sequence: 2 givenname: Abdelghani surname: Dahou fullname: Dahou, Abdelghani – sequence: 3 givenname: Mohammed A. A. orcidid: 0000-0002-6956-7641 surname: Al-qaness fullname: Al-qaness, Mohammed A. A. – sequence: 4 givenname: Songfeng orcidid: 0000-0003-4489-2488 surname: Lu fullname: Lu, Songfeng – sequence: 5 givenname: Mohamed Abd orcidid: 0000-0002-7682-6269 surname: Abd Elaziz fullname: Abd Elaziz, Mohamed Abd
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Keywords	intrusion detection system swarm Intelligence internet of things (IoT) Aquila optimizer sustainable computing cybersecurity feature selection
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SubjectTerms	Accuracy Aquila optimizer Classification Cybersecurity Datasets Deep learning Exploitation Feature selection Internet of Things intrusion detection system Intrusion detection systems Machine learning Neural networks Optimization algorithms Optimization techniques Support vector machines sustainable computing swarm Intelligence
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Title	Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System
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