#
anomaly
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A curated list of awesome anomaly detection resources
machine-learning
awesome
deep-learning
machinelearning
anomaly
anomalydetection
anomaly-detection
awesome-anomaly-detection
awesomeanomalydetection
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Updated
Jun 15, 2021
Kibana Alert & Report App for Elasticsearch
visualization
plugin
pdf
alarm
elasticsearch
alert
kibana
timeseries
scheduler
reporting
alerting
elk
watcher
elastic
kibi
anomaly
watchdog
kibana-dashboard
anomaly-detection
kaae
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Updated
Jan 28, 2021 - JavaScript
An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana
python
iot
elasticsearch
data-science
alerts
kibana
dashboard
timeseries
jupyter
sklearn
data-stream
datascience
dataset
machinelearning
anomaly
anomalydetection
anomalydiscovery
anomaly-detection
bokeh-dashboard
dsio
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Updated
Mar 31, 2020 - Python
arnaudvl
commented
May 6, 2021
tf.keras.Sequential is an instance of tf.keras.Model so could be left out across types in alibi-detect.
Deep Anomaly Detection with Outlier Exposure (ICLR 2019)
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Feb 16, 2021 - Python
Anomaly detection
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Updated
Jul 22, 2021 - Python
Tidy anomaly detection
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Updated
Jun 16, 2021 - R
williewheeler
commented
Oct 29, 2019
In the following code
@Override
public DetectorDocument findByUuid(String uuid) {
val queryBuilder = QueryBuilders.termQuery("uuid", uuid);
val searchSourceBuilder = elasticsearchUtil.getSourceBuilder(queryBuilder).size(DEFAULT_ES_RESULTS_SIZE);
val searchRequest = elasticsearchUtil.getSearchRequest(searchSourceBuilder, DETECTOR_INDEX, DETECTOR_DOC_TYPE)
Anomaly detection tutorial on univariate time series with an auto-encoder
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Mar 19, 2021 - Jupyter Notebook
Examples of not obvious behaviors for javascript beginner programmers
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Jun 12, 2019
Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)
python
machine-learning
real-time
outliers
intrusion-detection
outlier-detection
anomaly
unsupervised-learning
streaming-data
incremental-learning
fraud-detection
anomaly-detection
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Updated
Sep 8, 2020 - Python
Anomaly detection on SQL data warehouses and databases
bigquery
sql
snowflake
redshift
anomaly
anomalydetection
datawarehouse
anomaly-detection
timeseries-analysis
timeseries-forecasting
prophet-facebook
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Updated
Jul 26, 2021 - JavaScript
2D Outlier Analysis using Shiny
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Jun 30, 2016 - R
Feature Space Singularity for Out-of-Distribution Detection. (SafeAI 2021)
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Feb 15, 2021 - Python
CoRA Docs
data-science
data-visualization
forensics
dna
pairs
data-analytics
anomaly
anthropology
bones
commingled-human-remains
pathology
osteology
trauma
cora
articulations
human-remains
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Jun 9, 2021 - HTML
NETS:Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing
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Aug 25, 2020 - Java
Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping
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Apr 27, 2021 - Java
Main component extraction for outlier detection
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Dec 6, 2020 - Jupyter Notebook
Listen to the soothing sounds of attacks and anomalies detected by the Signal Sciences web protection platform.
go
api
golang
monitoring
attack
waf
sound
anomaly
rasp
signalsciences
signal-sciences
signal-sciences-api
sigsci
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Oct 1, 2018 - Go
Anomaly Detection In An IoT-Acquired Environmental Sensor Data
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Oct 2, 2018 - Python
whotwagner
commented
Jul 14, 2021
RELOAD: Rapid EvaLuation Of Anomaly Detectors @ UNIFI
security
machine-learning
data-mining
framework
monitoring
machine-learning-algorithms
data-analysis
machinelearning
anomaly
anomalydetection
dependability
anomaly-detection
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Updated
May 31, 2021 - HTML
Anomaly Detection using ELK (Elasticsearch, Logstash and Kibana)
elasticsearch
kibana
logstash
elk
bluemix
anomaly
anomalydetection
anomalydiscovery
anomaly-detection
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Updated
Mar 21, 2016 - Ruby
Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in strict semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpassing current baseline methods. Our semi-supervised anomaly detection method allows to inspect large amounts of data for anomalies across various applications.
data-mining
paper
semi-supervised-learning
anomaly
anomaly-detection
tensorflow2
ecml-pkdd
activation-analysis
activation-anomaly-analysis
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Updated
Apr 22, 2021 - Python
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The official instructions say to use joblib for pickling PyOD models.
This fails for AutoEncoders, or any other TensorFlow-backed model as far as I can tell. The error is:
Note that it's not sufficient to save the underlying Keras S