Real-time Bayesian anomaly detection in streaming environmental data

2009 ◽  
Vol 45 (4) ◽  
Author(s):  
David J. Hill ◽  
Barbara S. Minsker ◽  
Eyal Amir
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Environmental Data

Real-Time Anomaly Detection from Environmental Data Streams

2015 ◽  
pp. 125-144 ◽  
Author(s):  
Sergio Trilles ◽  
Sven Schade ◽  
Óscar Belmonte ◽  
Joaquín Huerta
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Data Streams ◽  
Environmental Data

scholarly journals Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Feature Detection ◽  
Trading System ◽  
Judgment Accuracy ◽  
Energy Trading ◽  
Real Time Processing ◽  
Detection Model ◽  
Time Required ◽  
The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

scholarly journals Anomaly Detection and Attack Classification for Train Real-Time Ethernet

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 22528-22541
Author(s):  
Ruifeng Duo ◽  
Xiaobo Nie ◽  
Ning Yang ◽  
Chuan Yue ◽  
Yongxiang Wang
Keyword(s):  
Anomaly Detection ◽  
Real Time

A LOW-COST MULTIHOP WIRELESS SENSOR NETWORK, ENABLING REAL-TIME MANAGEMENT OF ENVIRONMENTAL DATA FOR THE GREENHOUSE AND NURSERY INDUSTRY

2008 ◽  
pp. 523-530 ◽  
Author(s):  
J.D. Lea-Cox ◽  
A.G. Ristvey ◽  
F. Arguedas Rodriguez ◽  
D.S. Ross ◽  
J. Anhalt ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Time Management ◽  
Low Cost ◽  
Environmental Data ◽  
Wireless Sensor ◽  
Multihop Wireless
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