{"repo":"ait-aecid/logdata-anomaly-miner","free":true,"listed":false,"github":"https://github.com/ait-aecid/logdata-anomaly-miner","clone":"git clone https://github.com/ait-aecid/logdata-anomaly-miner.git","description":"This tool parses log data and allows to define analysis pipelines for anomaly detection. It was designed to run the analysis with limited resources and lowest possible permissions to make it suitable for production server use.","language":"Python","stars":93,"topics":["logging","anomaly","linux","security","logdata","python","detection","ids","anomaly-detection","cybersecurity"],"license":"GPL-3.0","category":"security-tools","readme_excerpt":"logdata-anomaly-miner This project is no longer maintained. Please use DetectMate instead. This tool parses log data and allows to define analysis pipelines for anomaly detection. It was designed to run the analysis with limited resources and lowest possible permissions to make it suitable for production server use. Requirements In order to install logdata-anomaly-miner a Linux system with python = 3.6 is required. All Ubuntu and Debian versions that we have in the tests are currently recommended. There is only experimental support for Fedora . More specifically the tested systems include Debian Buster, Debian Bullseye, Debian Bookworm, Ubuntu 20.04, Ubuntu 22.04, Fedora (docker image fedora:latest), and RedHat (docker image redhat/ubi9). See requirements.txt for further module dependencies Installation Debian There are Debian packages for logdata-anomaly-miner in the official Debian/Ubuntu repositories. From source The following command will install the latest stable release: Docker For installation with Docker see: Deployment with Docker Getting started Here are some resources to read in order to get started with configurations: Getting started) Some available configurations Documentation Wiki Publications Publications and talks: Landauer M., Wurzenberger M., Skopik F., Hotwagner W., Höld G. (2023): AMiner: A Modular Log Data Analysis Pipeline for Anomaly-based Intrusion Detection. Digital Threats: Research and Practice, Volume 4, Issue 1. March 2023, pp. 1–16, ACM. \\[PDF\\]","default_branch":null,"files":null,"tree":[],"storefront":"/r/ait-aecid","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/ait-aecid/logdata-anomaly-miner/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}