Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments
The Internet of Things (IoT) is no longer limited to single personalities, but rather, it is a perceptions that has widely increased and spread in some applications or fields. The mechanism for communicating between IoT devices similarity works as traditional communication between hosts. However, t...
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College of Computer and Information Technology – University of Wasit, Iraq
2024-12-01
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| Series: | Wasit Journal of Computer and Mathematics Science |
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| Online Access: | http://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/323 |
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| author | Hanan Abbas Mohammad |
| author_facet | Hanan Abbas Mohammad |
| author_sort | Hanan Abbas Mohammad |
| collection | DOAJ |
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The Internet of Things (IoT) is no longer limited to single personalities, but rather, it is a perceptions that has widely increased and spread in some applications or fields. The mechanism for communicating between IoT devices similarity works as traditional communication between hosts. However, the growing use of IoT has been gaining the interest of a growing number of attackers. Hence, a number of researchers are attempting to build an intrusion detection system utilizing machine learning and deep learning algorithms. In this work, a novel attack detection model is proposed by superimposing Whale Optimization Algorithm and Bidirectional Long Short-Term Memory (WB-LSTM) together. There are numerous deep learning competencies, but LSTM is one of the ones used to interpret big data or time series data. But, it is not easy to find what is the best weights for LSTMs in order to directly achieve performance. The LSTM results were 99.1%. Hence, in this work, we introduce the WOA-LSTM hybrid model, that utilizes WOA for finding the optimal weights for a network based on LSTM, and is used to detect the IoT attacks. The 99.98% was obtained from the WOA-LSTM hybrid model.
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| format | Article |
| id | doaj-art-49cf71f43ee541da9ae7fc5fd472826d |
| institution | Kabale University |
| issn | 2788-5879 2788-5887 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | College of Computer and Information Technology – University of Wasit, Iraq |
| record_format | Article |
| series | Wasit Journal of Computer and Mathematics Science |
| spelling | doaj-art-49cf71f43ee541da9ae7fc5fd472826d2025-01-30T05:23:42ZengCollege of Computer and Information Technology – University of Wasit, IraqWasit Journal of Computer and Mathematics Science2788-58792788-58872024-12-013410.31185/wjcms.323Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT EnvironmentsHanan Abbas Mohammad0University of Kirkuk The Internet of Things (IoT) is no longer limited to single personalities, but rather, it is a perceptions that has widely increased and spread in some applications or fields. The mechanism for communicating between IoT devices similarity works as traditional communication between hosts. However, the growing use of IoT has been gaining the interest of a growing number of attackers. Hence, a number of researchers are attempting to build an intrusion detection system utilizing machine learning and deep learning algorithms. In this work, a novel attack detection model is proposed by superimposing Whale Optimization Algorithm and Bidirectional Long Short-Term Memory (WB-LSTM) together. There are numerous deep learning competencies, but LSTM is one of the ones used to interpret big data or time series data. But, it is not easy to find what is the best weights for LSTMs in order to directly achieve performance. The LSTM results were 99.1%. Hence, in this work, we introduce the WOA-LSTM hybrid model, that utilizes WOA for finding the optimal weights for a network based on LSTM, and is used to detect the IoT attacks. The 99.98% was obtained from the WOA-LSTM hybrid model. http://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/323CICIoT2023 Dataset, Feature scaling, IoT Security, Deep learning, LSTM Models, Whale optimization algorithm (WOA), Detect IoT Attacks. |
| spellingShingle | Hanan Abbas Mohammad Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments Wasit Journal of Computer and Mathematics Science CICIoT2023 Dataset, Feature scaling, IoT Security, Deep learning, LSTM Models, Whale optimization algorithm (WOA), Detect IoT Attacks. |
| title | Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments |
| title_full | Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments |
| title_fullStr | Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments |
| title_full_unstemmed | Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments |
| title_short | Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments |
| title_sort | hybrid deep learning techniques for improved anomaly detection in iot environments |
| topic | CICIoT2023 Dataset, Feature scaling, IoT Security, Deep learning, LSTM Models, Whale optimization algorithm (WOA), Detect IoT Attacks. |
| url | http://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/323 |
| work_keys_str_mv | AT hananabbasmohammad hybriddeeplearningtechniquesforimprovedanomalydetectioniniotenvironments |