A machine learning toolkit for log-based anomaly detection [ISSRE'16]

A list of awesome research on log analysis, anomaly detection, fault localization, and AIOps

log anomaly detection toolkit including DeepLog

Paper list of log-based anomaly detection

Log Anomaly Detection - Machine learning to detect abnormal events logs

log anomaly detection via BERT

PyTorch implementation of Deeplog: Anomaly detection and diagnosis from system logs through deep learning

GAIA, with the full name Generic AIOps Atlas, is an overall dataset for analyzing operation problems such as anomaly detection, log analysis, fault localization, etc.

A deep learning toolkit for log-based anomaly detection

Log-based Anomaly Detection with Deep Learning: How Far Are We? (ICSE 2022, Technical Track)

LogLLM: Log-based Anomaly Detection Using Large Language Models (system log anomaly detection)

Log-based Anomaly Detection Without Log Parsing (ASE 2021, Research Track)

PyTorch implements "DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning"

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.

Analysis scripts for log data sets used in anomaly detection.

Implementation of PLELog in ICSE 2021 accepted paper:Semi-supervised Log-based Anomaly Detection via Probabilistic Label Estimation.

Anomaly detection in network traffic and event logs using deep learning (w/ Pytorch)

Workshop exercise materials for re:Invent 2017 - SID 341: Using AWS CloudTrail Logs for Scalable, Automated Anomaly Detection

[AAAI 2024] LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection

Log anomaly detection model using a CNN with TF-IDF and sliding window feature extraction.

AI-powered Log Intelligence System - Semantic search, anomaly detection, and root cause analysis for logs using RAG techniques. Built with Rust, Axum, NATS, ClickHouse, and Qdrant.

AutoLog: A Log Sequence Synthesis Framework for Anomaly Detection [ASE'23]

An deep learning based lightweight TCN for log anomaly detection.

Ensemble framework of some log based anomaly detection work.

DeepLog-Anomaly Detection and Diagnosis from System Logs through Deep Learning

LogLead performs log loading, log enhancement, log feature engineering, log analysis, log anomaly detection.

AIOps can be applied to log analysis using Python, ML specifically for **anomaly detection** in system logs.

Investigating attacks using Splunk Enterprise logs and creating SPL intrusion detection searches based on known attacker TTPs and anomaly behavior derived from statistical baselines

Code for the ISSRE-2024 Research Track paper: Leveraging RAG-Enhanced Large Language Model for Semi-Supervised Log Anomaly Detection

Scripts to deploy virtual testbed for log data analysis and anomaly detection.