A powerful streaming log template miner based on the Drain algorithm in golang
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Updated
Oct 23, 2024 - Go
A powerful streaming log template miner based on the Drain algorithm in golang
go-drain3 is a golang port of drain3, which is log parsing library written in python.
🚀 An intelligent, LLM-enhanced log parser pipeline that converts multi-format raw logs into structured JSON, learns from missed patterns, and evolves using Drain3 & open-source LLMs.
Local-first, privacy-respecting AI-assisted security log triage: Drain3 clustering, MITRE ATT&CK rules, Ollama/cloud LLMs
Security log triage CLI — deterministic detection + LLM explanations in Brazilian Portuguese. Scales to multi-GB logs, LLM used only for the explanation layer.
AI-driven log anomaly detector: Drain3 + Isolation Forest / autoencoder + ELK stack, with one-command demo.
ASDIP — AI Secure Data Intelligence Platform. Miniature SIEM with a 7-stage detection pipeline, Drain3 log clustering, stateful brute-force correlation, secret masking and live SSE risk scoring.
Hybrid log parser (Drain3 + LLM) for production log analysis
Agentic AI debug assistant for post-silicon validation logs: parse (Drain3), classify & dedup failures, cluster families (HDBSCAN), retrieve similar incidents (RAG), and name an evidence-backed root cause with Claude or a rule-based agent. CLI, REST API, Streamlit dashboard.
Find the most likely root cause in a Linux log, with the exact log lines as proof. Drain3 + Chroma + local LLM + plain-code evidence check.
Universal log pre-processing framework: ECS normalisation, ML enrichment, Merkle integrity, SIEM forwarding
Original AIOps prototype for streaming log parsing, immutable model versioning, hot rollback, and anomaly detection.
Incident flight recorder: turns logs, alerts, deploys and Slack into a minute-by-minute timeline, ranks likely triggers, and drafts an evidence-linked postmortem. FastAPI + Drain3 + React.
A production-ready TypeScript/Node.js streaming log template miner — 1:1 port of the official Python Drain3 algorithm. Extracts structured templates from unstructured logs in real time using fixed-depth prefix tree clustering with zero runtime dependencies.
Autonomous SOC log anomaly detection platform using Bi-LSTM with Multi-Head Self-Attention and Adaptive Gaussian KDE thresholding (-60.7% False Positive Rate on HDFS logs).
AIOps log anomaly detection on HDFS and BGL (15.9M lines): Drain parsing, 7 models from PCA to LSTM/Transformer on chronological splits, streaming replay (F1 0.982) and a FastAPI + React dashboard.
Network-Aware Event Sequence Modeling for User-Behavior and System Log Anomaly Detection | CSoNet 2026 | Springer LNCS (forthcoming)
One triage pipeline for logs, GitHub issues, CI failures and status incidents: deterministic clustering, one LLM verdict per cluster, a rule table decides.
Reduces gigabytes of logs to a small, representative sample, as a CLI or a Python library.
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