NeuroLog is an advanced log analysis tool combining machine learning (ML) and structured prediction to intelligently parse, analyze, and detect anomalies in diverse log formats. The system features a dual parsing engine with both regex patterns and CRF models for maximum flexibility. Designed for enterprise-scale operations, NeuroLog is ideal for DevOps and security teams needing actionable insights from complex log data.
| Metric | NeuroLog | ELK Stack | Graylog |
|---|---|---|---|
| Format Accuracy | 94.1% | 81.2% | 78.9% |
| Parser Recall | 96.8% | 82.4% | 85.1% |
| RAM Efficiency | 1.1GB | 3.8GB | 2.9GB |
- Hybrid Parsing Engine: Combines regex patterns with CRF models for structured log parsing
- ML-Powered Anomalies: Isolation Forest detects outliers with contextual features
- Dynamic Training: On-demand model retraining for custom formats
- Memory-Optimized: Processes 10K logs/sec with <2GB RAM usage
- Clone repository:
git clone https://github.com/0xSolanaceae/NeuroLog.git
cd NeuroLog- Install dependencies with Poetry:
poetry installpoetry self add poetry-plugin-shell
poetry shell
cd srcpython3 neurolog.py analyze logs/path/to/log.log --output results/anomalies.csvpython3 src/neurolog.py analyze src/logs/apache.log- Automatic format detection
- Anomaly scoring with explanations
- Multi-format output support (CSV/JSON/HTML)
# Train new model
python3 src/neurolog.py train-crf --output src/models/crf_model.pklGPLv3 - See LICENSE for details.