Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning
Communication in cyber-physical systems relies heavily on Wireless Sensor Networks (WSNs), which have numerous uses including ambient monitoring, object recognition, and data transmission. However, they are vulnerable to cyberattacks because they are connected to the IoT. In order to combat the diff...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10379637/ |
| _version_ | 1797360195621355520 |
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| author | Hadeel M. Saleh Hend Marouane Ahmed Fakhfakh |
| author_facet | Hadeel M. Saleh Hend Marouane Ahmed Fakhfakh |
| author_sort | Hadeel M. Saleh |
| collection | DOAJ |
| description | Communication in cyber-physical systems relies heavily on Wireless Sensor Networks (WSNs), which have numerous uses including ambient monitoring, object recognition, and data transmission. However, they are vulnerable to cyberattacks because they are connected to the IoT. In order to combat the difficulties associated with WSN intrusion detection, this research employs machine learning techniques, notably the Gaussian Nave Bayes (GNB) and Stochastic Gradient Descent (SGD) algorithms. The effectiveness of recommendation systems is improved with the introduction of context awareness. To lessen the burden on the computer, we first do a principal component analysis and singular value decomposition on the raw traffic data. On the WSN-DS dataset, the suggested SG-IDS model achieved a 96% accuracy rate, outperforming state-of-the-art algorithms with higher rates of 98% accuracy, 96% recall, and 97% F1 measurement. In an evaluation of an IoMT dataset, the SG-IDS performed admirably, with an accuracy of 0.87 and a precision of 1.00 in intrusion detection tasks. |
| first_indexed | 2024-03-08T15:35:52Z |
| format | Article |
| id | doaj.art-a64a67fe2442457e854e8fd5cac98b88 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T15:35:52Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a64a67fe2442457e854e8fd5cac98b882024-01-10T00:05:51ZengIEEEIEEE Access2169-35362024-01-01123825383610.1109/ACCESS.2023.334924810379637Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine LearningHadeel M. Saleh0https://orcid.org/0000-0001-6611-1916Hend Marouane1Ahmed Fakhfakh2Continuing Education Center, University of Anbar, Ramadi, IraqNational School of Electronics and Telecommunications (ENET'COM), NTS'COM Laboratory, Safax University, Sfax, TunisiaDigital and Numeric Research Center of Safax (CRNS), Sfax, TunisiaCommunication in cyber-physical systems relies heavily on Wireless Sensor Networks (WSNs), which have numerous uses including ambient monitoring, object recognition, and data transmission. However, they are vulnerable to cyberattacks because they are connected to the IoT. In order to combat the difficulties associated with WSN intrusion detection, this research employs machine learning techniques, notably the Gaussian Nave Bayes (GNB) and Stochastic Gradient Descent (SGD) algorithms. The effectiveness of recommendation systems is improved with the introduction of context awareness. To lessen the burden on the computer, we first do a principal component analysis and singular value decomposition on the raw traffic data. On the WSN-DS dataset, the suggested SG-IDS model achieved a 96% accuracy rate, outperforming state-of-the-art algorithms with higher rates of 98% accuracy, 96% recall, and 97% F1 measurement. In an evaluation of an IoMT dataset, the SG-IDS performed admirably, with an accuracy of 0.87 and a precision of 1.00 in intrusion detection tasks.https://ieeexplore.ieee.org/document/10379637/Intrusion detectionwireless sensor networkmachine learningaccuracyInternet of Things |
| spellingShingle | Hadeel M. Saleh Hend Marouane Ahmed Fakhfakh Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning IEEE Access Intrusion detection wireless sensor network machine learning accuracy Internet of Things |
| title | Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning |
| title_full | Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning |
| title_fullStr | Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning |
| title_full_unstemmed | Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning |
| title_short | Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning |
| title_sort | stochastic gradient descent intrusions detection for wireless sensor network attack detection system using machine learning |
| topic | Intrusion detection wireless sensor network machine learning accuracy Internet of Things |
| url | https://ieeexplore.ieee.org/document/10379637/ |
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