Anomaly Detection
Showing 16 of 52 projects
A Go library implementing essential machine learning algorithms including linear regression, logistic regression, and neural networks.
Suricata rules for network anomaly detection and threat hunting.
A Python probabilistic programming framework for objective model selection in time-varying parameter time series models.
A Clojure library implementing Hierarchical Temporal Memory (HTM) for temporal sequence learning and prediction.
An open-source framework for developing large-scale anomaly detection models using Apache Spark.
A low-interaction client honeypot that detects malicious websites using signature, anomaly, and pattern matching techniques.
A comprehensive Random Forest implementation in Go with extensions for deep forests, continuous learning, feature selection, and anomaly detection.
Clojure bindings for the BigML.io API, enabling machine learning tasks and pipelines from Clojure code.
Demo code for analyzing AWS CloudTrail and S3 logs using Apache Spark to detect security anomalies and enable SQL queries.
An end-to-end Python system for time series data analysis with in-database machine learning using TDengine.
A self-hosted, single-container Docker monitoring dashboard with real-time metrics, anomaly detection, and Telegram alerts.
An open-source 4D spatiotemporal AI platform built on PostgreSQL, PostGIS, TimescaleDB, H3, and pgvector for unified geospatial and time-series data analysis.
A D3.js utility for applying Nelson rules of process control to detect statistical anomalies in data.
A vendor-neutral, declarative data quality engine that defines checks in YAML and runs anywhere.
An AI-powered Docker log monitoring agent that uses LLMs to analyze container logs, detect anomalies, and provide contextual insights.
A specialized Linux-based operating system for paranormal investigation, environmental monitoring, and multi-modal anomaly detection research.
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