An Anomaly Detection Method Based on GCN and Correlation of High Dimensional Sensor Data in Power Grid System

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

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Anomaly Detection Method for Spacecraft System Based on High Dimensional Space Mapping

Journal of Physics Conference Series ◽

10.1088/1742-6596/2006/1/012069 ◽

2021 ◽

Vol 2006 (1) ◽

pp. 012069

Author(s):

Xiangyan Zhang ◽

Zhiqiang Li

Keyword(s):

Anomaly Detection ◽

Detection Method ◽

Dimensional Space ◽

Space Mapping ◽

High Dimensional ◽

High Dimensional Space

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A Real-Time Detection Method for Abnormal Data of Internet of Things Sensors Based on Mobile Edge Computing

Mathematical Problems in Engineering ◽

10.1155/2021/6655346 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Xuguang Liu

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Data Processing ◽

Real Time ◽

Detection Method ◽

Edge Computing ◽

Sensor Data ◽

Detection Accuracy ◽

Spatiotemporal Correlation ◽

Real Time Detection

Aiming at the anomaly detection problem in sensor data, traditional algorithms usually only focus on the continuity of single-source data and ignore the spatiotemporal correlation between multisource data, which reduces detection accuracy to a certain extent. Besides, due to the rapid growth of sensor data, centralized cloud computing platforms cannot meet the real-time detection needs of large-scale abnormal data. In order to solve this problem, a real-time detection method for abnormal data of IoT sensors based on edge computing is proposed. Firstly, sensor data is represented as time series; K-nearest neighbor (KNN) algorithm is further used to detect outliers and isolated groups of the data stream in time series. Secondly, an improved DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm is proposed by considering spatiotemporal correlation between multisource data. It can be set according to sample characteristics in the window and overcomes the slow convergence problem using global parameters and large samples, then makes full use of data correlation to complete anomaly detection. Moreover, this paper proposes a distributed anomaly detection model for sensor data based on edge computing. It performs data processing on computing resources close to the data source as much as possible, which improves the overall efficiency of data processing. Finally, simulation results show that the proposed method has higher computational efficiency and detection accuracy than traditional methods and has certain feasibility.

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A High-Dimensional and Small-Sample Submersible Fault Detection Method Based on Feature Selection and Data Augmentation

Sensors ◽

10.3390/s22010204 ◽

2021 ◽

Vol 22 (1) ◽

pp. 204

Author(s):

Penghui Zhao ◽

Qinghe Zheng ◽

Zhongjun Ding ◽

Yi Zhang ◽

Hongjun Wang ◽

...

Keyword(s):

Feature Selection ◽

Fault Detection ◽

Data Augmentation ◽

Detection Method ◽

Small Sample ◽

Sensor Data ◽

Generative Adversarial Networks ◽

High Dimensional ◽

Detection Accuracy ◽

Data Generation

The fault detection of manned submersibles plays a very important role in protecting the safety of submersible equipment and personnel. However, the diving sensor data is scarce and high-dimensional, so this paper proposes a submersible fault detection method, which is made up of feature selection module based on hierarchical clustering and Autoencoder (AE), the improved Deep Convolutional Generative Adversarial Networks (DCGAN)-based data augmentation module and fault detection module using Convolutional Neural Network (CNN) with LeNet-5 structure. First, feature selection is developed to select the features that have a strong correlation with failure event. Second, data augmentation model is conducted to generate sufficient data for training the CNN model, including rough data generation and data refiners. Finally, a fault detection framework with LeNet-5 is trained and fine-tuned by synthetic data, and tested using real data. Experiment results based on sensor data from submersible hydraulic system demonstrate that our proposed method can successfully detect the fault samples. The detection accuracy of proposed method can reach 97% and our method significantly outperforms other classic detection algorithms.

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