Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT
With the continuous increase in Internet of Things (IoT) device usage, more interest has been shown in internet security, specifically focusing on protecting these vulnerable devices from malicious traffic. Such threats are difficult to distinguish, so an advanced intrusion detection system (IDS) is...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Journal of Sensor and Actuator Networks |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2224-2708/12/2/29 |
| _version_ | 1797604740364763136 |
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| author | Dhiaa Musleh Meera Alotaibi Fahd Alhaidari Atta Rahman Rami M. Mohammad |
| author_facet | Dhiaa Musleh Meera Alotaibi Fahd Alhaidari Atta Rahman Rami M. Mohammad |
| author_sort | Dhiaa Musleh |
| collection | DOAJ |
| description | With the continuous increase in Internet of Things (IoT) device usage, more interest has been shown in internet security, specifically focusing on protecting these vulnerable devices from malicious traffic. Such threats are difficult to distinguish, so an advanced intrusion detection system (IDS) is becoming necessary. Machine learning (ML) is one of the promising techniques as a smart IDS in different areas, including IoT. However, the input to ML models should be extracted from the IoT environment by feature extraction models, which play a significant role in the detection rate and accuracy. Therefore, this research aims to introduce a study on ML-based IDS in IoT, considering different feature extraction algorithms with several ML models. This study evaluated several feature extractors, including image filters and transfer learning models, such as VGG-16 and DenseNet. Additionally, several machine learning algorithms, including random forest, K-nearest neighbors, SVM, and different stacked models were assessed considering all the explored feature extraction algorithms. The study presented a detailed evaluation of all combined models using the IEEE Dataport dataset. Results showed that VGG-16 combined with stacking resulted in the highest accuracy of 98.3%. |
| first_indexed | 2024-03-11T04:51:02Z |
| format | Article |
| id | doaj.art-6c9ebd296265472b8804d4a1a0d65587 |
| institution | Directory Open Access Journal |
| issn | 2224-2708 |
| language | English |
| last_indexed | 2024-03-11T04:51:02Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Sensor and Actuator Networks |
| spelling | doaj.art-6c9ebd296265472b8804d4a1a0d655872023-11-17T20:01:12ZengMDPI AGJournal of Sensor and Actuator Networks2224-27082023-03-011222910.3390/jsan12020029Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoTDhiaa Musleh0Meera Alotaibi1Fahd Alhaidari2Atta Rahman3Rami M. Mohammad4Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaSAUDI ARAMCO Cybersecurity Chair, Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaSAUDI ARAMCO Cybersecurity Chair, Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaWith the continuous increase in Internet of Things (IoT) device usage, more interest has been shown in internet security, specifically focusing on protecting these vulnerable devices from malicious traffic. Such threats are difficult to distinguish, so an advanced intrusion detection system (IDS) is becoming necessary. Machine learning (ML) is one of the promising techniques as a smart IDS in different areas, including IoT. However, the input to ML models should be extracted from the IoT environment by feature extraction models, which play a significant role in the detection rate and accuracy. Therefore, this research aims to introduce a study on ML-based IDS in IoT, considering different feature extraction algorithms with several ML models. This study evaluated several feature extractors, including image filters and transfer learning models, such as VGG-16 and DenseNet. Additionally, several machine learning algorithms, including random forest, K-nearest neighbors, SVM, and different stacked models were assessed considering all the explored feature extraction algorithms. The study presented a detailed evaluation of all combined models using the IEEE Dataport dataset. Results showed that VGG-16 combined with stacking resulted in the highest accuracy of 98.3%.https://www.mdpi.com/2224-2708/12/2/29intrusion detection systemInternet of Thingsfeature extractorsmachine learning |
| spellingShingle | Dhiaa Musleh Meera Alotaibi Fahd Alhaidari Atta Rahman Rami M. Mohammad Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT Journal of Sensor and Actuator Networks intrusion detection system Internet of Things feature extractors machine learning |
| title | Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT |
| title_full | Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT |
| title_fullStr | Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT |
| title_full_unstemmed | Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT |
| title_short | Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT |
| title_sort | intrusion detection system using feature extraction with machine learning algorithms in iot |
| topic | intrusion detection system Internet of Things feature extractors machine learning |
| url | https://www.mdpi.com/2224-2708/12/2/29 |
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