Anomaly Detection
Showing 36 of 41 projects
An open-source, real-time infrastructure monitoring platform with per-second metrics, ML-powered anomaly detection, and zero-configuration deployment.
An open-source, low-code Python library that automates end-to-end machine learning workflows.
A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
A Python library for user-friendly forecasting and anomaly detection on time series, from ARIMA to deep neural networks.
A flexible framework for alerting on anomalies, spikes, or patterns in Elasticsearch data.
A simple framework for alerting on anomalies, spikes, or other patterns in Elasticsearch data.
An open-source implementation of Hierarchical Temporal Memory (HTM) for anomaly detection and prediction of streaming data.
An open-source library for building massively scalable machine learning pipelines on Apache Spark.
A library built on Apache Spark for defining unit tests to measure data quality in large datasets.
An R package for robust anomaly detection in time series and vectors, handling seasonality and trend.
A curated list of research papers, datasets, and resources for anomaly detection in time-series, video, and image data.
A large collection of real-world system log datasets for AI-driven log analytics research.
A Python library for outlier, adversarial, and drift detection in machine learning models, supporting tabular, text, image, and time series data.
A curated collection of academic papers on data mining and machine learning techniques for fraud detection across various domains.
A lightweight Python library for anomaly detection and correlation in time series data, enabling root cause analysis.
A Python machine learning package for incremental learning on streaming data with concept drift detection.
A scalable library for exploring, validating, and monitoring machine learning data, integrated with TensorFlow and TFX.
An open-source ML-powered analytics engine for automated outlier detection and root cause analysis on high-dimensional metrics.
An open-source ML-powered analytics engine for automated outlier detection and root cause analysis on high-dimensional metrics.
A real-time anomaly detection algorithm for dynamic graph streams, identifying intrusions, fraud, and fake ratings with constant memory and update time.
An R package for detecting statistically significant breakpoints in time series using robust energy statistics.
An AutoML implementation and tutorial for automating machine learning pipelines on both static datasets and dynamic data streams, with a focus on IoT anomaly detection.
An open-source framework for detecting command and control communication through network traffic analysis using Zeek logs.
A network fingerprinting standard that identifies SSH client and server implementations via MD5 hashes of algorithm sets.
A Java library for static malware analysis of Portable Executable files with robust handling of malformations.
A high-performance data profiler for discovering and validating complex patterns in datasets, enabling data cleaning and quality analysis.
A high-performance data profiler for discovering and validating complex patterns like functional dependencies, inclusion dependencies, and association rules.
A tidy R package for detecting anomalies in time series data using decomposition and statistical methods.
An official Java port of Numenta's Hierarchical Temporal Memory (HTM) library for machine intelligence and anomaly detection.
A distributed, scalable database built for stream processing applications on Apache Kafka using SQL syntax.
Visualizes AWS IAM and Organizations as a graph using Neo4j to identify security anomalies and privilege escalation paths.
A flexible anomaly detection framework for Kapacitor using fingerprinting algorithms and lossy counting.
A distributed Spark/Scala implementation of Isolation Forest and Extended Isolation Forest algorithms for scalable unsupervised outlier detection.
An end-to-end Python outlier detection system with database support, automated machine learning, and unified APIs for statistical, ML, and deep learning models.
A Go library implementing essential machine learning algorithms including linear regression, logistic regression, and neural networks.
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