README.md

---
library_name: transformers
license: mit
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
---

# 🛡️ SIEM Multisource Log Generator

<p align="center">
  <img src="https://img.shields.io/badge/Transformers-HuggingFace-yellow" />
  <img src="https://img.shields.io/badge/Base%20Model-Mistral--7B-blue" />
  <img src="https://img.shields.io/badge/License-MIT-green" />
  <img src="https://img.shields.io/badge/Domain-Cybersecurity-red" />
</p>

---

## 🚀 Why This Model?

Security teams need **large volumes of realistic SIEM data** to build, test, and validate detections — but real logs are often **sensitive, restricted, or unavailable**.

The **SIEM Multisource Log Generator** produces **high-quality synthetic security logs** that resemble real-world telemetry across multiple sources, enabling safer experimentation, faster iteration, and better analyst training without touching production data.

---

## 📌 Model Summary

The **SIEM Multisource Log Generator** is a transformer-based language model fine-tuned to generate **synthetic Security Information and Event Management (SIEM) logs**. It is designed for cybersecurity research, detection engineering, SOC training, and SIEM validation workflows in non-production environments.

---

## 🧠 Model Details

- **Developed by:** Adarsh Ranjan  
- **Model type:** Transformer-based causal language model  
- **Base model:** `mistralai/Mistral-7B-Instruct-v0.3`  
- **Language:** English  
- **License:** MIT  
- **Framework:** 🤗 Hugging Face Transformers  
- **Model format:** Safetensors  

---

## 📄 Associated Papers

This model is fine-tuned from **Mistral 7B** and is grounded in the following foundational research:

- **Mistral 7B**  
  *Mistral 7B* — Efficient, high-performance open-weight language model  
  https://huggingface.co/papers/2310.06825

- **Instruction Tuning & Open Foundation Models**  
  *Direct Preference Optimization and Instruction-Following Models*  
  https://huggingface.co/papers/2305.14314

These papers describe the base architecture and training philosophy underlying the model.

---

## 📖 What Does It Generate?

The model produces **structured and semi-structured SIEM-style logs**, including signals from:

- 🔐 Authentication and identity systems  
- 🌐 Firewalls and network devices  
- 💻 Endpoint and host-based agents  

All outputs are **fully synthetic** and safe for testing and research.

---

## 🎯 Intended Use

### ✅ Direct Use
- Synthetic SIEM log generation  
- Detection rule and alert testing  
- Security analytics experimentation  
- SOC analyst training and simulations  

### 🔁 Downstream Use
- Fine-tuning for organization-specific log formats  
- Integration into SIEM test or staging environments  

### 🚫 Out-of-Scope Use
- Production ingestion of real security logs  
- Automated security decisions without human oversight  
- Real-world attack execution or facilitation  

---

## ⚠️ Bias, Risks, and Limitations

- Synthetic logs may not fully capture real attacker behavior  
- Rare or advanced attack techniques may be underrepresented  
- Benchmarks are qualitative and task-oriented  
- Outputs should be reviewed by cybersecurity professionals  

---

## 🚀 Getting Started

### 📦 Installation

```bash
pip install transformers safetensors
```

### 🧪 Basic Usage Example

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "adarsh-aur/siem-multisource-log-generator"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = (
    "Generate SIEM logs for a suspicious login scenario.\n"
    "Include timestamp, source IP, username, host, and outcome."
)

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=256)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

## 🧠 Chain-of-Thought–Safe Prompting (Recommended)

To remain policy-safe and improve output quality, **avoid asking for reasoning or explanations**.  
Instead, request **structured outputs directly**.

### ✅ Preferred
```
Generate SIEM logs showing a brute-force login attempt.
Return only the logs in JSON format.
```

### ❌ Avoid
```
Explain step by step how an attacker performs a brute-force attack.
```

The model is optimized for **output generation**, not procedural reasoning.

---

## 🧩 Prompt Templates

### 🔐 Authentication Anomaly
```
Generate SIEM logs for multiple failed login attempts followed by a success.
Include timestamp, username, source IP, host, and result.
```

### 🌐 Firewall Activity
```
Generate firewall logs showing blocked outbound traffic to malicious IPs.
Include rule_id, destination_ip, port, protocol, and action.
```

### 💻 Endpoint Detection
```
Generate endpoint logs for suspicious PowerShell execution.
Include process_name, command_line, parent_process, and severity.
```

---

## 📈 Detection Rule Examples

### Example: Brute Force Detection (Pseudo-SPL)

```
index=auth_logs action=failure
| stats count by src_ip, user
| where count > 5
```

### Example: Suspicious PowerShell Execution

```
index=endpoint_logs process_name="powershell.exe"
| search command_line="*EncodedCommand*"
```

These rules can be validated using synthetic logs generated by this model.

---

## 🔬 Benchmarks (Qualitative)

| Task                          | Result Summary                     |
|------------------------------|------------------------------------|
| Log Structure Consistency    | High                               |
| Field Coherence              | High                               |
| Scenario Diversity           | Medium–High                        |
| Detection Rule Compatibility | High                               |

> Note: Benchmarks are qualitative and based on domain inspection. No automated scoring metrics are published.

---

## 🧾 Dataset Card (Embedded)

### Dataset Description
- **Type:** Synthetic text-based SIEM logs
- **Sources:** Authentication, network, endpoint-style events
- **Sensitive Data:** None (fully synthetic)

### Dataset Usage
- SIEM testing and validation
- Detection engineering
- Cybersecurity research and education

### Dataset Limitations
- May not reflect organization-specific schemas
- Rare attack patterns may be underrepresented

---

## 🏋️ Training Details

Exact training datasets, preprocessing steps, and hyperparameters have not been publicly disclosed.  
The model is assumed to be fine-tuned on curated or synthetic SIEM-style log text.

---

## 🌱 Environmental Impact

Training-related carbon emissions were not recorded.

Environmental impact can be estimated using:  
Lacoste et al. (2019), *Quantifying the Carbon Emissions of Machine Learning*  
https://arxiv.org/abs/1910.09700

---

## ⚙️ Technical Specifications

- **Architecture:** Transformer-based causal language model  
- **Objective:** Synthetic SIEM log generation  
- **Software:** Python, PyTorch, Hugging Face Transformers  
- **Hardware:** Not publicly documented  

---

## 📚 Citation

```bibtex
@misc{siem_multisource_log_generator,
  title={SIEM Multisource Log Generator},
  author={Adarsh Ranjan},
  year={2025},
  howpublished={Hugging Face Model Hub},
  url={https://huggingface.co/adarsh-aur/siem-multisource-log-generator}
}
```

---

## 👤 Model Card Author

Adarsh Ranjan

---

## 💬 Contact

For questions, feedback, or contributions, please use the Hugging Face model repository discussion page.