Research on intrusion detection method for edge networks based on federated reinforcement learning
With the rapid proliferation of Internet of things (IoT) devices, the frequency and intensity of attacks targeting these devices are constantly increasing. Therefore, it's quite important that security mechanisms are continuously updated to ensure the safety of IoT devices. However, as public a...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
China InfoCom Media Group
2024-12-01
|
| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00442/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832586345285943296 |
|---|---|
| author | DING Kai HUANG Yidu TAO Ming XIE Renping |
| author_facet | DING Kai HUANG Yidu TAO Ming XIE Renping |
| author_sort | DING Kai |
| collection | DOAJ |
| description | With the rapid proliferation of Internet of things (IoT) devices, the frequency and intensity of attacks targeting these devices are constantly increasing. Therefore, it's quite important that security mechanisms are continuously updated to ensure the safety of IoT devices. However, as public awareness of privacy grows, many datasets are no longer shared, leading to the emergence of data silos, which hinders the improvement of IoT security. To address this issue, a federated reinforcement learning-based intrusion detection method was proposed, and experiments were conducted using two datasets from the Internet of medical things (IoMT) and Internet of vehicles (IoV) scenarios. Imbalanced traffic sample distributions were designed for each edge agent to simulate a real-world environment, allowing for the evaluation of the detection accuracy and robustness of the global model. Double deep Q-network (DDQN) was employed as the reinforcement learning framework for the edge agents, and the experimental results were evaluated using accuracy, precision, recall, and F1-score. The results demonstrate that the proposed method exhibits strong robustness and detection accuracy. |
| format | Article |
| id | doaj-art-797532f4d43947c481aaefbc367902aa |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-797532f4d43947c481aaefbc367902aa2025-01-25T19:00:28ZzhoChina InfoCom Media Group物联网学报2096-37502024-12-01814015579606324Research on intrusion detection method for edge networks based on federated reinforcement learningDING KaiHUANG YiduTAO MingXIE RenpingWith the rapid proliferation of Internet of things (IoT) devices, the frequency and intensity of attacks targeting these devices are constantly increasing. Therefore, it's quite important that security mechanisms are continuously updated to ensure the safety of IoT devices. However, as public awareness of privacy grows, many datasets are no longer shared, leading to the emergence of data silos, which hinders the improvement of IoT security. To address this issue, a federated reinforcement learning-based intrusion detection method was proposed, and experiments were conducted using two datasets from the Internet of medical things (IoMT) and Internet of vehicles (IoV) scenarios. Imbalanced traffic sample distributions were designed for each edge agent to simulate a real-world environment, allowing for the evaluation of the detection accuracy and robustness of the global model. Double deep Q-network (DDQN) was employed as the reinforcement learning framework for the edge agents, and the experimental results were evaluated using accuracy, precision, recall, and F1-score. The results demonstrate that the proposed method exhibits strong robustness and detection accuracy.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00442/federated reinforcement learningintrusion detectionIoT securityIoT |
| spellingShingle | DING Kai HUANG Yidu TAO Ming XIE Renping Research on intrusion detection method for edge networks based on federated reinforcement learning 物联网学报 federated reinforcement learning intrusion detection IoT security IoT |
| title | Research on intrusion detection method for edge networks based on federated reinforcement learning |
| title_full | Research on intrusion detection method for edge networks based on federated reinforcement learning |
| title_fullStr | Research on intrusion detection method for edge networks based on federated reinforcement learning |
| title_full_unstemmed | Research on intrusion detection method for edge networks based on federated reinforcement learning |
| title_short | Research on intrusion detection method for edge networks based on federated reinforcement learning |
| title_sort | research on intrusion detection method for edge networks based on federated reinforcement learning |
| topic | federated reinforcement learning intrusion detection IoT security IoT |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00442/ |
| work_keys_str_mv | AT dingkai researchonintrusiondetectionmethodforedgenetworksbasedonfederatedreinforcementlearning AT huangyidu researchonintrusiondetectionmethodforedgenetworksbasedonfederatedreinforcementlearning AT taoming researchonintrusiondetectionmethodforedgenetworksbasedonfederatedreinforcementlearning AT xierenping researchonintrusiondetectionmethodforedgenetworksbasedonfederatedreinforcementlearning |