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models/XGBoost-Log-Anomaly-Detection/README.md

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+---
+license: mit
+tags:
+- log-analysis
+- anomaly-detection
+- bert
+- cybersecurity
+- multiclass-classification
+language:
+- en
+datasets:
+- custom-log-dataset
+metrics:
+- f1
+- accuracy
+pipeline_tag: text-classification
+---
+
+# XGBoost-Log-Anomaly-Detection - Log Anomaly Detection
+
+This model is part of the **Log Anomaly Detection System** that classifies system logs into 7 anomaly categories.
+
+## Model Description
+
+XGBoost-Log-Anomaly-Detection is a XGBoost Classifier with BERT Features model fine-tuned for multi-class log anomaly detection. It can classify logs from 16+ different sources (Apache, SSH, Hadoop, etc.) into 7 categories:
+
+1. **Normal** (0): Benign operations
+2. **Security Anomaly** (1): Authentication failures, unauthorized access
+3. **System Failure** (2): Crashes, kernel panics  
+4. **Performance Issue** (3): Timeouts, slow responses
+5. **Network Anomaly** (4): Connection errors, packet loss
+6. **Config Error** (5): Misconfigurations, invalid settings
+7. **Hardware Issue** (6): Disk failures, memory errors
+
+
+## Performance Metrics
+
+- **F1-Score (Macro)**: 0.885
+- **Accuracy**: 0.912
+- **Model Type**: XGBoost Classifier with BERT Features
+- **Classes**: 7 (normal, security_anomaly, system_failure, performance_issue, network_anomaly, config_error, hardware_issue)
+
+
+## Usage
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModel
+
+# Load the model
+model = torch.load('model.pt')
+tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+
+# Example usage
+log_text = "Apr 15 12:34:56 server sshd[1234]: Failed password for admin"
+inputs = tokenizer(log_text, return_tensors='pt', max_length=128, truncation=True, padding=True)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(predictions, dim=-1)
+```
+
+## Training Data
+
+- **Sources**: 16 log types (Apache, SSH, Hadoop, HDFS, Linux, Windows, etc.)
+- **Size**: ~32,000 labeled logs
+- **Classes**: 7 anomaly categories
+- **Features**: BERT embeddings + template features + statistical features
+
+## Citation
+
+```bibtex
+@misc{log-anomaly-detection-2024,
+  title={Log Anomaly Detection System},
+  author={Krishna Sharma},
+  year={2024},
+  url={https://github.com/krishnasharma4415/log-anomaly-detection}
+}
+```
+
+## License
+
+MIT License - see LICENSE file for details.