A novel federated learning aggregation algorithm for AIoT intrusion detection
Abstract Nowadays, the development of Artificial Intelligence of Things (AIoT) is advancing rapidly, and intelligent devices are increasingly exposed to more security risks on the network. Deep learning‐based intrusion detection is an effective security defence approach. Federated learning (FL) is c...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-04-01
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| Series: | IET Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cmu2.12744 |
| _version_ | 1797201750927605760 |
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| author | Yidong Jia Fuhong Lin Yan Sun |
| author_facet | Yidong Jia Fuhong Lin Yan Sun |
| author_sort | Yidong Jia |
| collection | DOAJ |
| description | Abstract Nowadays, the development of Artificial Intelligence of Things (AIoT) is advancing rapidly, and intelligent devices are increasingly exposed to more security risks on the network. Deep learning‐based intrusion detection is an effective security defence approach. Federated learning (FL) is capable of enabling deep learning models to be trained on local clients without uploading their data to a central server. This paper proposes a novel federated learning aggregation algorithm called fed‐dynamic gravitational search algorithm (Fed‐DGSA), which incorporates the GSA algorithm to optimize the weight updating process of FL local models. During the updating process, the decay rate of the gravity coefficient is optimized and random perturbations and dynamic weights are introduced to ensure a more stable and efficient FL aggregation process. The experimental results show that the detection accuracy of Fed‐DGSA has reached about 97.8%, and it is demonstrated that the model trained using Fed‐DGSA achieves higher accuracy compared to Fed‐Avg. |
| first_indexed | 2024-04-24T07:52:31Z |
| format | Article |
| id | doaj.art-b63fc36f1cf14048accb0c737abfb66f |
| institution | Directory Open Access Journal |
| issn | 1751-8628 1751-8636 |
| language | English |
| last_indexed | 2024-04-24T07:52:31Z |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj.art-b63fc36f1cf14048accb0c737abfb66f2024-04-18T10:22:08ZengWileyIET Communications1751-86281751-86362024-04-0118742943610.1049/cmu2.12744A novel federated learning aggregation algorithm for AIoT intrusion detectionYidong Jia0Fuhong Lin1Yan Sun2Department of Computer and Communication EngineeringUniversity of Science and Technology BeijingBeijing ChinaDepartment of Computer and Communication EngineeringUniversity of Science and Technology BeijingBeijing ChinaChina Industrial Control Systems Cyber Emergency Response Team Beijing ChinaAbstract Nowadays, the development of Artificial Intelligence of Things (AIoT) is advancing rapidly, and intelligent devices are increasingly exposed to more security risks on the network. Deep learning‐based intrusion detection is an effective security defence approach. Federated learning (FL) is capable of enabling deep learning models to be trained on local clients without uploading their data to a central server. This paper proposes a novel federated learning aggregation algorithm called fed‐dynamic gravitational search algorithm (Fed‐DGSA), which incorporates the GSA algorithm to optimize the weight updating process of FL local models. During the updating process, the decay rate of the gravity coefficient is optimized and random perturbations and dynamic weights are introduced to ensure a more stable and efficient FL aggregation process. The experimental results show that the detection accuracy of Fed‐DGSA has reached about 97.8%, and it is demonstrated that the model trained using Fed‐DGSA achieves higher accuracy compared to Fed‐Avg.https://doi.org/10.1049/cmu2.12744computer network securityfederated learningInternet of Things |
| spellingShingle | Yidong Jia Fuhong Lin Yan Sun A novel federated learning aggregation algorithm for AIoT intrusion detection IET Communications computer network security federated learning Internet of Things |
| title | A novel federated learning aggregation algorithm for AIoT intrusion detection |
| title_full | A novel federated learning aggregation algorithm for AIoT intrusion detection |
| title_fullStr | A novel federated learning aggregation algorithm for AIoT intrusion detection |
| title_full_unstemmed | A novel federated learning aggregation algorithm for AIoT intrusion detection |
| title_short | A novel federated learning aggregation algorithm for AIoT intrusion detection |
| title_sort | novel federated learning aggregation algorithm for aiot intrusion detection |
| topic | computer network security federated learning Internet of Things |
| url | https://doi.org/10.1049/cmu2.12744 |
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