APF-PF: The new real-time anomaly detection model of massive log flow

2016 ◽  
Author(s):  
Qing Wu ◽  
Guoping Wu
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Detection Model

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).

Automated Real Time Anomaly Detection Model for Operation and Production Data at Scale

2020 ◽  
Author(s):  
Iqmal Irsyad Mohammad Fuad ◽  
Mohd Fuad Ngah Demon ◽  
Husiyandi Husni
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Production Data ◽  
Detection Model

A novel abnormal detection method for bearing temperature based on spatiotemporal fusion

2021 ◽  
pp. 095440972110221
Author(s):  
Yong Zhi Liu ◽  
Yi Sheng Zou ◽  
Yu Wu ◽  
Hao Yang Zhang ◽  
Guo Fu Ding
Keyword(s):  
Decision Making ◽  
Anomaly Detection ◽  
Real Time ◽  
Early Warning ◽  
High Speed ◽  
Entropy Method ◽  
High Speed Train ◽  
Detection Model ◽  
Time Prediction ◽  
Model Based

The existing bearing temperature fault detection and early warning system has a high false alarm rate and insufficient early warning ability. For this reason, in this study, a method for detecting the abnormal bearing temperature of high-speed trains based on spatiotemporal fusion decision-making was proposed. First, the temperature characteristics of similar bearings were compared and analyzed with different spatial distributions. Then, a bearing abnormal temperature rise detection model based on the analytic hierarchy process (AHP) entropy method was proposed. Second, the temperature characteristics of the same bearings were compared and analyzed with different time distributions. A real-time prediction model of high-speed train bearing temperature anomalies based on Bi-directional Long Short-Term Memory (BILSTM) was proposed. Finally, the D-S evidence theory was used to combine the anomaly detection model based on the AHP entropy method and the anomaly detection model based on BILSTM real-time prediction. Through the comprehensive diagnosis and decision-making of high-speed train bearings from two dimensions of space and time, a more comprehensive and accurate anomaly detection model was realized. The experimental results showed that the spatiotemporal comparison fusion decision model successfully eliminated the misjudgment phenomenon of single-dimension model diagnosis and that it has good early warning ability.

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

scholarly journals An Efficient Distributed Anomaly Detection Model for Wireless Sensor Networks

2013 ◽  
Vol 5 ◽  
pp. 9-14 ◽  
Author(s):  
Murad A. Rassam ◽  
Anazida Zainal ◽  
Mohd Aizaini Maarof
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Wireless Sensor ◽  
Detection Model
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