通信学报2024，Vol.45Issue(4)：13-26,14.DOI:10.11959/j.issn.1000-436x.2024011
基于内存增强自编码器的轻量级无人机网络异常检测模型
Lightweight anomaly detection model for UAV networks based on memory-enhanced autoencoders
摘要
Abstract
In order to solve the problems of high energy consumption and high reliance on manual annotation data of tra-ditional 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 pro-cess was reduced in an incremental update manner.The hierarchical clustering algorithm was used to divide the compos-ite statistical features,and the separated features were input to multiple small memory-enhanced autoencoders in the inte-grated 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 pub-lic 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.关键词
无人机网络/异常检测/轻量级在线检测/内存增强自编码器Key words
UAV network/anomaly detection/lightweight online detection/memory-augmented autoencoder分类
信息技术与安全科学引用本文复制引用
胡天柱,沈玉龙,任保全,何吉,刘成梁,李洪钧..基于内存增强自编码器的轻量级无人机网络异常检测模型[J].通信学报,2024,45(4):13-26,14.基金项目
国家自然科学基金资助项目(No.62220106004,No.61972308) （No.62220106004,No.61972308）
国家自然科学基金重大研究计划基金资助项目(No.92267204) （No.92267204）
陕西省重点研发计划基金资助项目(No.2022KXJ-093,No.2021ZDLGY07-05) （No.2022KXJ-093,No.2021ZDLGY07-05）
陕西省创新能力支撑计划基金资助项目(No.2023-CX-TD-02)The National Natural Science Foundation of China(No.62220106004,No.61972308),Major Research Plan of the National Natural Science Foundation of China(No.92267204),The Key Research and Development Program of Shaanxi Province(No.2022KXJ-093,No.2021ZDLGY07-05),Innovation Capability Support Program of Shaanxi(No.2023-CX-TD-02) （No.2023-CX-TD-02）
