Classification of DNS Queries for Anomaly Detection

2013 ◽  
Author(s):  
Hongbo Shi ◽  
Kazuhiko Iwasaki
Keyword(s):  
Anomaly Detection

Detection and Classification of Sensor Anomalies in Gas Turbine Field Data

2018 ◽  
Author(s):  
Giuseppe Fabio Ceschini ◽  
Lucrezia Manservigi ◽  
Giovanni Bechini ◽  
Mauro Venturini
Keyword(s):  
Anomaly Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Field Data ◽  
Time Correlation ◽  
Detection Algorithm ◽  
Anomaly Classification ◽  
Monitoring And Diagnostics ◽  
Faulty Sensors

Anomaly detection and classification is a key challenge for gas turbine monitoring and diagnostics. To this purpose, a comprehensive approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named DCIDS) was developed by the authors in previous papers. The methodology consists of an Anomaly Detection Algorithm (ADA) and an Anomaly Classification Algorithm (ACA). The ADA identifies anomalies according to three different levels of filtering. Anomalies are subsequently analyzed by the ACA to perform their classification, according to time correlation, magnitude and number of sensors in which an anomaly is contemporarily identified. The performance of the DCIDS approach is assessed in this paper based on a significant amount of field data taken on several Siemens gas turbines in operation. The field data refer to six different physical quantities, i.e. vibration, pressure, temperature, VGV position, lube oil tank level and rotational speed. The analyses carried out in this paper allow the detection and classification of the anomalies and provide some rules of thumb for field operation, with the final aim of identifying time occurrence and magnitude of faulty sensors and measurements.

Anomaly detection and classification of objects in hyperspectral scene data

2009 ◽  
Author(s):  
Niclas Wadströmer ◽  
Ingmar Renhorn ◽  
Jörgen Ahlberg
Keyword(s):  
Anomaly Detection

scholarly journals Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

2015 ◽  
Vol 7 (10) ◽  
pp. 12680-12703 ◽  
Author(s):  
Borja Rodríguez-Cuenca ◽  
Silverio García-Cortés ◽  
Celestino Ordóñez ◽  
Maria Alonso
Keyword(s):  
Anomaly Detection ◽  
Point Cloud ◽  
Automatic Detection ◽  
Detection Algorithm ◽  
Point Cloud Data ◽  
Cloud Data

Anomaly detection using dynamic Neural Networks, classification of prestack data

2012 ◽  
Author(s):  
Kamal Hami-Eddine ◽  
Pascal Klein ◽  
Loic Richard ◽  
Andrew Furniss
Keyword(s):  
Neural Networks ◽  
Anomaly Detection ◽  
Dynamic Neural Networks
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