Due to the issue that existing wireless sensor network (WSN)-based anomaly detection methods only consider and analyze temporal features, in this paper, a self-supervised learning-based anomaly node detection method based on an autoencoder is designed. This method integrates temporal WSN data flow feature extraction, spatial position feature extraction and intermodal WSN correlation feature extraction into the design of the autoencoder to make full use of the spatial and temporal information of the WSN for anomaly detection. First, a fully connected network is used to extract the temporal features of nodes by considering a single mode from a local spatial perspective. Second, a graph neural network (GNN) is used to introduce the WSN topology from a global spatial perspective for anomaly detection and extract the spatial and temporal features of the data flows of nodes and their neighbors by considering a single mode. Then, the adaptive fusion method involving weighted summation is used to extract the relevant features between different models. In addition, this paper introduces a gated recurrent unit (GRU) to solve the long-term dependence problem of the time dimension. Eventually, the reconstructed output of the decoder and the hidden layer representation of the autoencoder are fed into a fully connected network to calculate the anomaly probability of the current system. Since the spatial feature extraction operation is advanced, the designed method can be applied to the task of large-scale network anomaly detection by adding a clustering operation. Experiments show that the designed method outperforms the baselines, and the F1 score reaches 90.6%, which is 5.2% higher than those of the existing anomaly detection methods based on unsupervised reconstruction and prediction. Code and model are available at https://github.com/GuetYe/anomaly_detection/GLSL
翻译:由于现有的无线传感器网络(WSN)异常点探测方法仅考虑和分析时间特征,本文件中设计了一个基于自动编码器的自监督的基于学习的异常节点探测方法。这种方法将时间的 WSN 数据流特征提取、空间位置提取和联运 WSN 相关特征提取纳入自动编码器的设计中,以便充分利用WSN 的空和时间信息来探测异常。首先,一个完全连接的网络通过从当地空间角度考虑单一模式来提取节点的时间特征。第二,使用一个基于学习的基于自动编码的异常节点探测方法(GNN),从全球空间角度引入WSN的基于学习的异常节点检测方法。这种方法将时间、时间、时间、时间、空间、空间、空间、空间、空间、空间、空间、空间、空间、地理等数据流数据流提取的时空特征。然后,使用包含加权加和时间和时间等相关特征的适应融合方法。此外,本文还引入一个基于时间层面长期依赖性的固定单位(GRUU) 。最后,从全球测算中重建了一次直径地GOreal 和直径的直径网络的运行输出,从当前直径解到当前直径解的直径解到当前直径解的直径解的直径解的直径径解的直径解算法。