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
胡天柱 1沈玉龙 2任保全 3何吉 4刘成梁 4李洪钧3
作者信息
- 1. 西安电子科技大学网络与信息安全学院,陕西 西安 710126;军事科学院系统工程研究院,北京 100070
- 2. 西安电子科技大学计算机科学与技术学院,陕西 西安 710126
- 3. 军事科学院系统工程研究院,北京 100070
- 4. 军事科学院系统工程研究院,北京 100070;西安电子科技大学计算机科学与技术学院,陕西 西安 710126
- 折叠
摘要
为了解决传统智能攻击检测方法在无人机网络中存在的高能耗以及高度依赖人工标注数据的问题,提出一种基于双层内存增强自编码器集成架构的轻量级无人机网络在线异常检测模型.采用基于操作系统的消息队列进行数据包缓存,实现对高速数据流的持久化处理,有效提升了模型的稳定性和可靠性.基于衰减窗口模型计算数据流复合统计特征,以增量更新方式降低了计算过程中的内存复杂度.利用层次聚类算法对复合统计特征进行划分,将分离的特征输入集成架构中的多个小型内存增强自编码器进行独立训练,降低了计算复杂度,同时解决了传统自编码器因重构效果过拟合而导致的漏报问题.在公开数据集和NS-3仿真数据集上的实验表明,所提模型在保证轻量级的同时,与基线方法相比,假阴性率分别平均降低了35.9%和48%.
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引用本文复制引用
基金项目
国家自然科学基金(62220106004)
国家自然科学基金(61972308)
国家自然科学基金重大研究计划(92267204)
陕西省重点研发计划(2022KXJ-093)
陕西省重点研发计划(2021ZDLGY07-05)
陕西省创新能力支撑计划(2023-CX-TD-02)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据