Lightweight anomaly detection model for UAV networks based on memory-enhanced autoencoders
In order to solve the problems of high energy consumption and high reliance on manual annotation data of traditional intelligent attack detection methods in UAV networks, a lightweight UAV network online anomaly detection model based on a double-layer memory-enhanced autoencoder integrated architect...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2024-04-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024011/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841539192289492992 |
|---|---|
| author | HU Tianzhu SHEN Yulong REN Baoquan HE Ji LIU Chengliang LI Hongjun |
| author_facet | HU Tianzhu SHEN Yulong REN Baoquan HE Ji LIU Chengliang LI Hongjun |
| author_sort | HU Tianzhu |
| collection | DOAJ |
| description | In order to solve the problems of high energy consumption and high reliance on manual annotation data of traditional intelligent attack detection methods in UAV networks, a lightweight UAV network online anomaly detection model based on a double-layer memory-enhanced autoencoder integrated architecture was proposed. The message queue based on the operating system was used for data packet caching to achieve persistent processing of high-speed data streams, which effectively improved the stability and reliability of the model. The composite statistical characteristics of the data flow were calculated based on the damped window model, and the memory complexity in the calculation process was reduced in an incremental update manner. The hierarchical clustering algorithm was used to divide the composite statistical features, and the separated features were input to multiple small memory-enhanced autoencoders in the integrated architecture for independent training, which reduced the computational complexity and solved the problem of false negatives caused by the overfitting of the reconstruction effect of the traditional autoencoder. Experiments on public data sets and NS-3 simulation data sets show that while ensuring lightweight, the proposed model reduces the false negative rate by an average of 35.9% and 48% compared with the baseline method. |
| format | Article |
| id | doaj-art-4fd0f03b714545b6b27588e0214bb1e7 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-04-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-4fd0f03b714545b6b27588e0214bb1e72025-01-14T07:24:16ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-04-0145132659255043Lightweight anomaly detection model for UAV networks based on memory-enhanced autoencodersHU TianzhuSHEN YulongREN BaoquanHE JiLIU ChengliangLI HongjunIn order to solve the problems of high energy consumption and high reliance on manual annotation data of traditional intelligent attack detection methods in UAV networks, a lightweight UAV network online anomaly detection model based on a double-layer memory-enhanced autoencoder integrated architecture was proposed. The message queue based on the operating system was used for data packet caching to achieve persistent processing of high-speed data streams, which effectively improved the stability and reliability of the model. The composite statistical characteristics of the data flow were calculated based on the damped window model, and the memory complexity in the calculation process was reduced in an incremental update manner. The hierarchical clustering algorithm was used to divide the composite statistical features, and the separated features were input to multiple small memory-enhanced autoencoders in the integrated architecture for independent training, which reduced the computational complexity and solved the problem of false negatives caused by the overfitting of the reconstruction effect of the traditional autoencoder. Experiments on public data sets and NS-3 simulation data sets show that while ensuring lightweight, the proposed model reduces the false negative rate by an average of 35.9% and 48% compared with the baseline method.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024011/UAV networkanomaly detectionlightweight online detectionmemory-augmented autoencoder |
| spellingShingle | HU Tianzhu SHEN Yulong REN Baoquan HE Ji LIU Chengliang LI Hongjun Lightweight anomaly detection model for UAV networks based on memory-enhanced autoencoders Tongxin xuebao UAV network anomaly detection lightweight online detection memory-augmented autoencoder |
| title | Lightweight anomaly detection model for UAV networks based on memory-enhanced autoencoders |
| title_full | Lightweight anomaly detection model for UAV networks based on memory-enhanced autoencoders |
| title_fullStr | Lightweight anomaly detection model for UAV networks based on memory-enhanced autoencoders |
| title_full_unstemmed | Lightweight anomaly detection model for UAV networks based on memory-enhanced autoencoders |
| title_short | Lightweight anomaly detection model for UAV networks based on memory-enhanced autoencoders |
| title_sort | lightweight anomaly detection model for uav networks based on memory enhanced autoencoders |
| topic | UAV network anomaly detection lightweight online detection memory-augmented autoencoder |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024011/ |
| work_keys_str_mv | AT hutianzhu lightweightanomalydetectionmodelforuavnetworksbasedonmemoryenhancedautoencoders AT shenyulong lightweightanomalydetectionmodelforuavnetworksbasedonmemoryenhancedautoencoders AT renbaoquan lightweightanomalydetectionmodelforuavnetworksbasedonmemoryenhancedautoencoders AT heji lightweightanomalydetectionmodelforuavnetworksbasedonmemoryenhancedautoencoders AT liuchengliang lightweightanomalydetectionmodelforuavnetworksbasedonmemoryenhancedautoencoders AT lihongjun lightweightanomalydetectionmodelforuavnetworksbasedonmemoryenhancedautoencoders |