Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods
Abstract This paper proposes an intelligent hybrid model that leverages machine learning and artificial intelligence to enhance the security of Wireless Sensor Networks (WSNs) by identifying and preventing cyberattacks. The study employs feature reduction techniques, including Singular Value Decompo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-01-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-023-00870-w |
| _version_ | 1797355728517726208 |
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| author | Mohamed H. Behiry Mohammed Aly |
| author_facet | Mohamed H. Behiry Mohammed Aly |
| author_sort | Mohamed H. Behiry |
| collection | DOAJ |
| description | Abstract This paper proposes an intelligent hybrid model that leverages machine learning and artificial intelligence to enhance the security of Wireless Sensor Networks (WSNs) by identifying and preventing cyberattacks. The study employs feature reduction techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), along with the K-means clustering model enhanced information gain (KMC-IG) for feature extraction. The Synthetic Minority Excessively Technique is introduced for data balancing, followed by intrusion detection systems and network traffic categorization. The research evaluates a deep learning-based feed-forward neural network algorithm's accuracy, precision, recall, and F-measure across three vital datasets: NSL-KDD, UNSW-NB 15, and CICIDS 2017, considering both full and reduced feature sets. Comparative analysis against benchmark machine learning approaches is also conducted. The proposed algorithm demonstrates exceptional performance, achieving high accuracy and reliability in intrusion detection for WSNs. The study outlines the system configuration and parameter settings, contributing to the advancement of WSN security. |
| first_indexed | 2024-03-08T14:15:14Z |
| format | Article |
| id | doaj.art-76f2442d8ef74a57bdfcaefb4a81a417 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-03-08T14:15:14Z |
| publishDate | 2024-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-76f2442d8ef74a57bdfcaefb4a81a4172024-01-14T12:26:16ZengSpringerOpenJournal of Big Data2196-11152024-01-0111113910.1186/s40537-023-00870-wCyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methodsMohamed H. Behiry0Mohammed Aly1Department of Artificial Intelligence, Faculty of Artificial Intelligence, Egyptian Russian UniversityDepartment of Artificial Intelligence, Faculty of Artificial Intelligence, Egyptian Russian UniversityAbstract This paper proposes an intelligent hybrid model that leverages machine learning and artificial intelligence to enhance the security of Wireless Sensor Networks (WSNs) by identifying and preventing cyberattacks. The study employs feature reduction techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), along with the K-means clustering model enhanced information gain (KMC-IG) for feature extraction. The Synthetic Minority Excessively Technique is introduced for data balancing, followed by intrusion detection systems and network traffic categorization. The research evaluates a deep learning-based feed-forward neural network algorithm's accuracy, precision, recall, and F-measure across three vital datasets: NSL-KDD, UNSW-NB 15, and CICIDS 2017, considering both full and reduced feature sets. Comparative analysis against benchmark machine learning approaches is also conducted. The proposed algorithm demonstrates exceptional performance, achieving high accuracy and reliability in intrusion detection for WSNs. The study outlines the system configuration and parameter settings, contributing to the advancement of WSN security.https://doi.org/10.1186/s40537-023-00870-wWSNHybrid reductionMLAINSL-KDDUNSW-NB 15 |
| spellingShingle | Mohamed H. Behiry Mohammed Aly Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods Journal of Big Data WSN Hybrid reduction ML AI NSL-KDD UNSW-NB 15 |
| title | Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods |
| title_full | Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods |
| title_fullStr | Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods |
| title_full_unstemmed | Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods |
| title_short | Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods |
| title_sort | cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with ai and machine learning methods |
| topic | WSN Hybrid reduction ML AI NSL-KDD UNSW-NB 15 |
| url | https://doi.org/10.1186/s40537-023-00870-w |
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