SLMAD: Statistical Learning-Based Metric Anomaly Detection

2021 ◽  
pp. 252-263
Author(s):  
Arsalan Shahid ◽  
Gary White ◽  
Jaroslaw Diuwe ◽  
Alexandros Agapitos ◽  
Owen O’Brien
Keyword(s):  
Anomaly Detection ◽  
Statistical Learning

scholarly journals System Log Detection Model Based on Conformal Prediction

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 232 ◽  
Author(s):  
Yitong Ren ◽  
Zhaojun Gu ◽  
Zhi Wang ◽  
Zhihong Tian ◽  
Chunbo Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Statistical Learning ◽  
The Internet ◽  
Learning Method ◽  
Log Data ◽  
Detection Model ◽  
Statistical Learning Method ◽  
The Internet Of Things

With the rapid development of the Internet of Things, the combination of the Internet of Things with machine learning, Hadoop and other fields are current development trends. Hadoop Distributed File System (HDFS) is one of the core components of Hadoop, which is used to process files that are divided into data blocks distributed in the cluster. Once the distributed log data are abnormal, it will cause serious losses. When using machine learning algorithms for system log anomaly detection, the output of threshold-based classification models are only normal or abnormal simple predictions. This paper used the statistical learning method of conformity measure to calculate the similarity between test data and past experience. Compared with detection methods based on static threshold, the statistical learning method of the conformity measure can dynamically adapt to the changing log data. By adjusting the maximum fault tolerance, a system administrator can better manage and monitor the system logs. In addition, the computational efficiency of the statistical learning method for conformity measurement was improved. This paper implemented an intranet anomaly detection model based on log analysis, and conducted trial detection on HDFS data sets quickly and efficiently.

Outlier Dirichlet Mixture Mechanism: Adversarial Statistical Learning for Anomaly Detection in the Fog

2019 ◽  
Vol 14 (8) ◽  
pp. 1975-1987 ◽  
Author(s):  
Nour Moustafa ◽  
Kim-Kwang Raymond Choo ◽  
Ibrahim Radwan ◽  
Seyit Camtepe
Keyword(s):  
Anomaly Detection ◽  
Statistical Learning ◽  
Dirichlet Mixture

scholarly journals Automatic Anomaly Detection on In-Production Manufacturing Machines Using Statistical Learning Methods

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2344 ◽  
Author(s):  
Federico Pittino ◽  
Michael Puggl ◽  
Thomas Moldaschl ◽  
Christina Hirschl
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Statistical Learning ◽  
Control Charts ◽  
Industry 4.0 ◽  
Production Environment ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
And Control

Anomaly detection is becoming increasingly important to enhance reliability and resiliency in the Industry 4.0 framework. In this work, we investigate different methods for anomaly detection on in-production manufacturing machines taking into account their variability, both in operation and in wear conditions. We demonstrate how the nature of the available data, featuring any anomaly or not, is of importance for the algorithmic choice, discussing both statistical machine learning methods and control charts. We finally develop methods for automatic anomaly detection, which obtain a recall close to one on our data. Our developed methods are designed not to rely on a continuous recalibration and hand-tuning by the machine user, thereby allowing their deployment in an in-production environment robustly and efficiently.

DAD: A Distributed Anomaly Detection system using ensemble one-class statistical learning in edge networks

2021 ◽  
Vol 118 ◽  
pp. 240-251
Author(s):  
Nour Moustafa ◽  
Marwa Keshk ◽  
Kim-Kwang Raymond Choo ◽  
Timothy Lynar ◽  
Seyit Camtepe ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Statistical Learning ◽  
Detection System ◽  
Edge Networks ◽  
Anomaly Detection System

Rapid Serial Auditory Presentation

2015 ◽  
Vol 62 (5) ◽  
pp. 346-351 ◽  
Author(s):  
Ana Franco ◽  
Julia Eberlen ◽  
Arnaud Destrebecqz ◽  
Axel Cleeremans ◽  
Julie Bertels
Keyword(s):  
Statistical Learning ◽  
Reaction Times ◽  
Visual Presentation ◽  
Detection Task ◽  
Speech Segmentation ◽  
Auditory Presentation ◽  
Indirect Measure ◽  
Speech Stream ◽  
Adult Participants ◽  
Presentation Procedure

Abstract. The Rapid Serial Visual Presentation procedure is a method widely used in visual perception research. In this paper we propose an adaptation of this method which can be used with auditory material and enables assessment of statistical learning in speech segmentation. Adult participants were exposed to an artificial speech stream composed of statistically defined trisyllabic nonsense words. They were subsequently instructed to perform a detection task in a Rapid Serial Auditory Presentation (RSAP) stream in which they had to detect a syllable in a short speech stream. Results showed that reaction times varied as a function of the statistical predictability of the syllable: second and third syllables of each word were responded to faster than first syllables. This result suggests that the RSAP procedure provides a reliable and sensitive indirect measure of auditory statistical learning.

Statistical Learning of Implicit Regularity Correlates With Acquisition of Chinese Literacy as a Second Language

2012 ◽  
Author(s):  
Denise H. Wu ◽  
Esther H.-Y. Shih ◽  
Ram Frost ◽  
Jun Ren Lee ◽  
Chiaying Lee ◽  
...  
Keyword(s):  
Second Language ◽  
Statistical Learning
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