Modeling of Botnet Detection Using Chaotic Binary Pelican Optimization Algorithm With Deep Learning on Internet of Things Environment
Nowadays, there are ample amounts of Internet of Things (IoT) devices interconnected to the networks, and with technological improvement, cyberattacks and security threads, for example, botnets, are rapidly evolving and emerging with high-risk attacks. A botnet is a network of compromised devices th...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10318038/ |
| _version_ | 1797473503466749952 |
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| author | Fadwa Alrowais Majdy M. Eltahir Sumayh S. Aljameel Radwa Marzouk Gouse Pasha Mohammed Ahmed S. Salama |
| author_facet | Fadwa Alrowais Majdy M. Eltahir Sumayh S. Aljameel Radwa Marzouk Gouse Pasha Mohammed Ahmed S. Salama |
| author_sort | Fadwa Alrowais |
| collection | DOAJ |
| description | Nowadays, there are ample amounts of Internet of Things (IoT) devices interconnected to the networks, and with technological improvement, cyberattacks and security threads, for example, botnets, are rapidly evolving and emerging with high-risk attacks. A botnet is a network of compromised devices that are controlled by cyber attackers, frequently employed to perform different cyberattacks. Such attack disrupts IoT evolution by disrupting services and networks for IoT devices. Detecting botnets in an IoT environment includes finding abnormal patterns or behaviors that might indicate the existence of these malicious networks. Several researchers have proposed deep learning (DL) and machine learning (ML) approaches for identifying and categorizing botnet attacks in the IoT platform. Therefore, this study introduces a Botnet Detection using the Chaotic Binary Pelican Optimization Algorithm with Deep Learning (BNT-CBPOADL) technique in the IoT environment. The main aim of the BNT-CBPOADL method lies in the correct detection and categorization of botnet attacks in the IoT environment. In the BNT-CBPOADL method, Z-score normalization is applied for pre-processing. Besides, the CBPOA technique is derived for feature selection. The convolutional variational autoencoder (CVAE) method is applied for botnet detection. At last, the arithmetical optimization algorithm (AOA) is employed for the optimal hyperparameter tuning of the CVAE algorithm. The experimental valuation of the BNT-CBPOADL technique is tested on a Bot-IoT database. The experimentation outcomes inferred the supremacy of the BNT-CBPOADL method over other existing techniques with maximum accuracy of 99.50%. |
| first_indexed | 2024-03-09T20:15:24Z |
| format | Article |
| id | doaj.art-54c85091db03417ea581fae036857266 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-09T20:15:24Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-54c85091db03417ea581fae0368572662023-11-24T00:01:27ZengIEEEIEEE Access2169-35362023-01-011113061813062610.1109/ACCESS.2023.333269010318038Modeling of Botnet Detection Using Chaotic Binary Pelican Optimization Algorithm With Deep Learning on Internet of Things EnvironmentFadwa Alrowais0https://orcid.org/0000-0002-8447-198XMajdy M. Eltahir1https://orcid.org/0000-0002-1810-4372Sumayh S. Aljameel2https://orcid.org/0000-0001-8246-4658Radwa Marzouk3https://orcid.org/0000-0001-6527-9856Gouse Pasha Mohammed4https://orcid.org/0000-0003-1583-9872Ahmed S. Salama5https://orcid.org/0000-0002-1066-8261Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Science & Art at Mahayil, King Khalid University, Abha, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, SAUDI ARAMCO Cybersecurity Chair, Imam Abdulrahman bin Faisal University, Dammam, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo, EgyptNowadays, there are ample amounts of Internet of Things (IoT) devices interconnected to the networks, and with technological improvement, cyberattacks and security threads, for example, botnets, are rapidly evolving and emerging with high-risk attacks. A botnet is a network of compromised devices that are controlled by cyber attackers, frequently employed to perform different cyberattacks. Such attack disrupts IoT evolution by disrupting services and networks for IoT devices. Detecting botnets in an IoT environment includes finding abnormal patterns or behaviors that might indicate the existence of these malicious networks. Several researchers have proposed deep learning (DL) and machine learning (ML) approaches for identifying and categorizing botnet attacks in the IoT platform. Therefore, this study introduces a Botnet Detection using the Chaotic Binary Pelican Optimization Algorithm with Deep Learning (BNT-CBPOADL) technique in the IoT environment. The main aim of the BNT-CBPOADL method lies in the correct detection and categorization of botnet attacks in the IoT environment. In the BNT-CBPOADL method, Z-score normalization is applied for pre-processing. Besides, the CBPOA technique is derived for feature selection. The convolutional variational autoencoder (CVAE) method is applied for botnet detection. At last, the arithmetical optimization algorithm (AOA) is employed for the optimal hyperparameter tuning of the CVAE algorithm. The experimental valuation of the BNT-CBPOADL technique is tested on a Bot-IoT database. The experimentation outcomes inferred the supremacy of the BNT-CBPOADL method over other existing techniques with maximum accuracy of 99.50%.https://ieeexplore.ieee.org/document/10318038/Internet of Thingsprivacysecuritybotnet detectionmetaheuristicsdeep learning |
| spellingShingle | Fadwa Alrowais Majdy M. Eltahir Sumayh S. Aljameel Radwa Marzouk Gouse Pasha Mohammed Ahmed S. Salama Modeling of Botnet Detection Using Chaotic Binary Pelican Optimization Algorithm With Deep Learning on Internet of Things Environment IEEE Access Internet of Things privacy security botnet detection metaheuristics deep learning |
| title | Modeling of Botnet Detection Using Chaotic Binary Pelican Optimization Algorithm With Deep Learning on Internet of Things Environment |
| title_full | Modeling of Botnet Detection Using Chaotic Binary Pelican Optimization Algorithm With Deep Learning on Internet of Things Environment |
| title_fullStr | Modeling of Botnet Detection Using Chaotic Binary Pelican Optimization Algorithm With Deep Learning on Internet of Things Environment |
| title_full_unstemmed | Modeling of Botnet Detection Using Chaotic Binary Pelican Optimization Algorithm With Deep Learning on Internet of Things Environment |
| title_short | Modeling of Botnet Detection Using Chaotic Binary Pelican Optimization Algorithm With Deep Learning on Internet of Things Environment |
| title_sort | modeling of botnet detection using chaotic binary pelican optimization algorithm with deep learning on internet of things environment |
| topic | Internet of Things privacy security botnet detection metaheuristics deep learning |
| url | https://ieeexplore.ieee.org/document/10318038/ |
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