ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1904.06646
  4. Cited By
Should I Raise The Red Flag? A comprehensive survey of anomaly scoring
  methods toward mitigating false alarms
v1v2 (latest)

Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms

14 April 2019
Zahra Zohrevand
U. Glässer
    AAML
ArXiv (abs)PDFHTML

Papers citing "Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms"

16 / 16 papers shown
Title
PyOD: A Python Toolbox for Scalable Outlier Detection
PyOD: A Python Toolbox for Scalable Outlier Detection
Yue Zhao
Zain Nasrullah
Zheng Li
89
724
0
06 Jan 2019
Active Anomaly Detection via Ensembles
Active Anomaly Detection via Ensembles
S. Das
M. R. Islam
Nitthilan Kanappan Jayakodi
J. Doppa
47
13
0
17 Sep 2018
Detecting Cyberattacks in Industrial Control Systems Using Convolutional
  Neural Networks
Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks
Moshe Kravchik
A. Shabtai
64
274
0
21 Jun 2018
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic
  Thresholding
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
K. Hundman
V. Constantinou
Christopher Laporte
Ian Colwell
T. Söderström
AI4TS
117
1,248
0
13 Feb 2018
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
M. Mohammadi
Ala I. Al-Fuqaha
Sameh Sorour
Mohsen Guizani
76
1,060
0
09 Dec 2017
Setting the threshold for high throughput detectors: A mathematical
  approach for ensembles of dynamic, heterogeneous, probabilistic anomaly
  detectors
Setting the threshold for high throughput detectors: A mathematical approach for ensembles of dynamic, heterogeneous, probabilistic anomaly detectors
Robert A. Bridges
Jessie D. Jamieson
Joel W. Reed
33
16
0
25 Oct 2017
Time Series Anomaly Detection; Detection of anomalous drops with limited
  features and sparse examples in noisy highly periodic data
Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data
Dominique T. Shipmon
Jason M. Gurevitch
Paolo Piselli
Stephen T. Edwards
AI4TS
43
131
0
11 Aug 2017
Mass Volume Curves and Anomaly Ranking
Mass Volume Curves and Anomaly Ranking
Stéphan Clémenccon
Albert Thomas
16
13
0
03 May 2017
Unsupervised Anomaly Detection with Generative Adversarial Networks to
  Guide Marker Discovery
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
T. Schlegl
Philipp Seeböck
S. Waldstein
U. Schmidt-Erfurth
Georg Langs
MedImGAN
106
2,230
0
17 Mar 2017
Deep Learning for Time-Series Analysis
Deep Learning for Time-Series Analysis
J. Gamboa
AI4TS
58
442
0
07 Jan 2017
How to Evaluate the Quality of Unsupervised Anomaly Detection
  Algorithms?
How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?
Nicolas Goix
28
72
0
05 Jul 2016
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly
  Benchmark
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark
Alexander Lavin
Subutai Ahmad
AI4TS
47
426
0
12 Oct 2015
A Meta-Analysis of the Anomaly Detection Problem
A Meta-Analysis of the Anomaly Detection Problem
Andrew Emmott
S. Das
Thomas G. Dietterich
Alan Fern
Weng-Keen Wong
65
174
0
03 Mar 2015
Sum-Product Networks: A New Deep Architecture
Sum-Product Networks: A New Deep Architecture
Hoifung Poon
Pedro M. Domingos
TPM
81
758
0
14 Feb 2012
Robust Kernel Density Estimation
Robust Kernel Density Estimation
JooSeuk Kim
Clayton D. Scott
OOD
72
390
0
15 Jul 2011
Learning under Concept Drift: an Overview
Learning under Concept Drift: an Overview
Indrė Žliobaitė
91
454
0
22 Oct 2010
1