library_name: transformers
license: mit
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
🛡️ SIEM Multisource Log Generator
🚀 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/2310.06825Instruction 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
pip install transformers safetensors
🧪 Basic Usage Example
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
@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.