scholarly journals A Survey of Anomaly Detection in Industrial Wireless Sensor Networks with Critical Water System Infrastructure as a Case Study

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2491 ◽  
Author(s):  
Daniel Ramotsoela ◽  
Adnan Abu-Mahfouz ◽  
Gerhard Hancke
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Critical Infrastructure ◽  
Water System ◽  
Machine Learning Techniques ◽  
Wireless Sensor ◽  
Monitoring And Control ◽  
Industrial Wireless Sensor Networks

The increased use of Industrial Wireless Sensor Networks (IWSN) in a variety of different applications, including those that involve critical infrastructure, has meant that adequately protecting these systems has become a necessity. These cyber-physical systems improve the monitoring and control features of these systems but also introduce several security challenges. Intrusion detection is a convenient second line of defence in case of the failure of normal network security protocols. Anomaly detection is a branch of intrusion detection that is resource friendly and provides broader detection generality making it ideal for IWSN applications. These schemes can be used to detect abnormal changes in the environment where IWSNs are deployed. This paper presents a literature survey of the work done in the field in recent years focusing primarily on machine learning techniques. Major research gaps regarding the practical feasibility of these schemes are also identified from surveyed work and critical water infrastructure is discussed as a use case.

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.

Sign in / Sign up

Export Citation Format

Share Document