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

Grab your DNS logs, detect anomalies, and finally understand what's happening on your network. The missing piece between DNS servers and your data stack.

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)

GeoLogonalyzer is a utility to analyze remote access logs for anomalies such as travel feasibility and data center sources.

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

Reduce logs to their semantic anomalies

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.

Cell tower logger and anomaly detector for Linux mobile devices. Monitors cellular connections, logs tower data with GPS, and detects suspicious activity.

A completely automated anomaly detector Zeek network flows files (conn.log).

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)

No description available

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.

No description available

Machine learning algorithms to detect anomalies in unstructured logs

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.

An Adaptive AI‑Powered Web Application Firewall for Django. Detects anomalies, blocks suspicious IPs, prevents UUID tampering, stops honeypot field exploits, and continuously improves via daily log-based retraining.