PratikDagale23/Ensemble-Model-for-Log-Based-Anomaly-Detection-Using-Deep-Learning
View on GitHub ↗Related repositories →This project uses deep learning and ensemble techniques to detect anomalies in system logs from HDFS, BGL, and Linux datasets. It includes preprocessing pipelines, advanced modeling, and rigorous evaluation to ensure high precision and adaptability. Ideal for system monitoring, security, and operational optimization.
active 2024-11-24 → 2025-02-11 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 80 days from 2024-11-24 to 2025-02-11. Pushes: 5 total, peak 4 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 1 total, peak 1 in a day.
- Pushes
- Pull requests
- Issues
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Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| PratikDagale23 | 4 | 4 | 0 | 0 |
| riyat28 | 1 | 1 | 0 | 0 |
Recent activity
Latest issues, pull requests and releases
No issue or PR events — this repo's activity is pushes only.
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 1 stars here means stars gained during the window, not the repo's star count.