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Existing distributed anomaly detection models based on federated learning can hardly deal with the balance between anomaly detection performance and data privacy protection. In this regard, a federated learning anomaly detection model is proposed based on adaptive noise and hybrid attention mechanism. First, built on the convolutional neural network, this model integrates spatial and multi-head hybrid attention mechanisms to extract complex features in a multidimensional and deep manner, enabling high-precision anomaly detection. Second, based on both local and centralized differential privacy, this model utilizes the adaptive noise and the privacy budget allocation to further improve the privacy and robustness. Validated experiments are exerted on public datasets NSL-KDD and UNSW-NB15. The results show that compared with the existing mainstream approaches, the proposed model can achieve higher-quality anomaly detection while ensuring user data privacy.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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