A Network Arena for Benchmarking AI Agents on Network Troubleshooting

An AI-Native Platform for Benchmarking SRE Agents

The LLM Evaluation Framework

An unsupervised machine learning pipeline for real-time anomaly detection in HDFS logs. Features automated log parsing with Drain3, Isolation Forest classification, and dynamic threshold optimizati…

Compass is a task diagnosis platform for bigdata

Detect anomalies in logging using machine learning models

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…

Analysis scripts for log data sets used in anomaly detection.

Final project for the T-725-MALV course at Reykjavik University (Fall 2024), exploring Large Language Models (LLAMA, BERT) for anomaly detection in system logs through fine-tuning and benchmarking …

Log based anomaly detection and fault localization system.

The final project of deep learning and practice (summer 2020) in NCTU.

Learning TileLang with 10 puzzles!

Network Log Visualization and Anomaly Detection

[ISSRE 2024, Research Track] LLMeLog: An Approach for Anomaly Detection based on LLM-enriched Log Events

Parse server logs by Drain algorithm. Integrate Transformer into anomaly detection network. Demonstrate better performance on public dataset.

Official code for PAKDD'25 paper "Adapting Large Language Models for Parameter-Efficient Log Anomaly Detection"

Comparative study of anomaly detection methods in logs

Log Sequence Anomaly Detection based on Template and Parameter Parsing via BERT

A highly scalable real-time log anomaly detection architecture with LLMs, information retrieval, and user feedback to pinpoint faults across a distributed system.

DeepTraLog: Trace-Log Combined Microservice Anomaly Detection through Graph-based Deep Learning

LogLead stands for Log Loader, Enhancer, and Anomaly Detector.

Log-based anomaly detection

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 suitab…

Enable beginners to become job-ready AIOps engineers by mastering monitoring, logs, metrics, ML-driven anomaly detection, automation, and incident intelligence — using GitHub as the central learnin…