Detecting IoT Attacks Using an Ensemble Machine Learning Model
Malicious attacks are becoming more prevalent due to the growing use of Internet of Things (IoT) devices in homes, offices, transportation, healthcare, and other locations. By incorporating fog computing into IoT, attacks can be detected in a short amount of time, as the distance between IoT devices...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/14/4/102 |
| _version_ | 1827619702251192320 |
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| author | Vikas Tomer Sachin Sharma |
| author_facet | Vikas Tomer Sachin Sharma |
| author_sort | Vikas Tomer |
| collection | DOAJ |
| description | Malicious attacks are becoming more prevalent due to the growing use of Internet of Things (IoT) devices in homes, offices, transportation, healthcare, and other locations. By incorporating fog computing into IoT, attacks can be detected in a short amount of time, as the distance between IoT devices and fog devices is smaller than the distance between IoT devices and the cloud. Machine learning is frequently used for the detection of attacks due to the huge amount of data available from IoT devices. However, the problem is that fog devices may not have enough resources, such as processing power and memory, to detect attacks in a timely manner. This paper proposes an approach to offload the machine learning model selection task to the cloud and the real-time prediction task to the fog nodes. Using the proposed method, based on historical data, an ensemble machine learning model is built in the cloud, followed by the real-time detection of attacks on fog nodes. The proposed approach is tested using the NSL-KDD dataset. The results show the effectiveness of the proposed approach in terms of several performance measures, such as execution time, precision, recall, accuracy, and ROC (receiver operating characteristic) curve. |
| first_indexed | 2024-03-09T10:35:57Z |
| format | Article |
| id | doaj.art-2c4395ac674244f1b3ae510db0b2446c |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-09T10:35:57Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-2c4395ac674244f1b3ae510db0b2446c2023-12-01T20:54:54ZengMDPI AGFuture Internet1999-59032022-03-0114410210.3390/fi14040102Detecting IoT Attacks Using an Ensemble Machine Learning ModelVikas Tomer0Sachin Sharma1Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun 248002, IndiaSchool of Electrical and Electronic Engineering, Technological University Dublin, D07 EWV4 Dublin, IrelandMalicious attacks are becoming more prevalent due to the growing use of Internet of Things (IoT) devices in homes, offices, transportation, healthcare, and other locations. By incorporating fog computing into IoT, attacks can be detected in a short amount of time, as the distance between IoT devices and fog devices is smaller than the distance between IoT devices and the cloud. Machine learning is frequently used for the detection of attacks due to the huge amount of data available from IoT devices. However, the problem is that fog devices may not have enough resources, such as processing power and memory, to detect attacks in a timely manner. This paper proposes an approach to offload the machine learning model selection task to the cloud and the real-time prediction task to the fog nodes. Using the proposed method, based on historical data, an ensemble machine learning model is built in the cloud, followed by the real-time detection of attacks on fog nodes. The proposed approach is tested using the NSL-KDD dataset. The results show the effectiveness of the proposed approach in terms of several performance measures, such as execution time, precision, recall, accuracy, and ROC (receiver operating characteristic) curve.https://www.mdpi.com/1999-5903/14/4/102Internet of Things (IoT)machine learningcybersecurityDDoS |
| spellingShingle | Vikas Tomer Sachin Sharma Detecting IoT Attacks Using an Ensemble Machine Learning Model Future Internet Internet of Things (IoT) machine learning cybersecurity DDoS |
| title | Detecting IoT Attacks Using an Ensemble Machine Learning Model |
| title_full | Detecting IoT Attacks Using an Ensemble Machine Learning Model |
| title_fullStr | Detecting IoT Attacks Using an Ensemble Machine Learning Model |
| title_full_unstemmed | Detecting IoT Attacks Using an Ensemble Machine Learning Model |
| title_short | Detecting IoT Attacks Using an Ensemble Machine Learning Model |
| title_sort | detecting iot attacks using an ensemble machine learning model |
| topic | Internet of Things (IoT) machine learning cybersecurity DDoS |
| url | https://www.mdpi.com/1999-5903/14/4/102 |
| work_keys_str_mv | AT vikastomer detectingiotattacksusinganensemblemachinelearningmodel AT sachinsharma detectingiotattacksusinganensemblemachinelearningmodel |