A Danger Model Based Anomaly Detection Method for Wireless Sensor Networks

2009 ◽  
Author(s):  
Kui Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Detection Method ◽  
Wireless Sensor ◽  
Model Based

DWT-based anomaly detection method for cyber security of wireless sensor networks

2016 ◽  
Vol 9 (15) ◽  
pp. 2911-2922 ◽  
Author(s):  
Łukasz Saganowski ◽  
Tomasz Andrysiak ◽  
Rafał Kozik ◽  
Michał Choraś
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Cyber Security ◽  
Detection Method ◽  
Wireless Sensor

scholarly journals An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

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