SIEM Log Generator - Mistral 7B QLoRA
A fine-tuned Mistral-7B model specialized in Security Information and Event Management (SIEM) log analysis and generation. This model has been trained using QLoRA (4-bit quantization) on multiple cybersecurity log sources to understand and generate security-related event data.
Model Description
This model is a specialized variant of Mistral-7B-Instruct fine-tuned for SIEM operations, including:
Network traffic analysis (DDoS detection, port scanning)
Authentication event monitoring (credential stuffing, brute force)
Cloud security events (AWS CloudTrail analysis)
System log interpretation
MITRE ATT&CK framework mapping
Training Data Sources
The model was trained on a diverse set of security logs:
Network Logs : CICIDS2017 dataset (DDoS, PortScan patterns)Authentication Logs : Risk-based authentication eventsSystem Logs : Linux/Unix syslog eventsCloud Logs : AWS CloudTrail security events
MITRE ATT&CK Coverage
The model recognizes and maps events to MITRE ATT&CK techniques:
T1499: Endpoint Denial of Service (DDoS)
T1046: Network Service Scanning
T1110: Brute Force
T1110.004: Credential Stuffing
T1078.004: Cloud Account Access
Training Details
Training Configuration
Base Model : mistralai/Mistral-7B-Instruct-v0.2Method : QLoRA (4-bit quantization with LoRA adapters)LoRA Rank : 8LoRA Alpha : 16Target Modules : q_proj, v_projTraining Samples : ~500 diverse security eventsBatch Size : 8Learning Rate : 5e-4Precision : bfloat16Training Steps : 50
Hardware
GPU : NVIDIA Tesla T4 (16GB VRAM)Platform : Kaggle NotebooksTraining Time : ~5-10 minutes
Usage
Installation
pip install transformers peft torch bitsandbytes accelerateLoading the Model
1 from transformers import AutoTokenizer , AutoModelForCausalLM
2 from peft import PeftModel
3 import torch
4
5 # Load base model with 4-bit quantization
6 base_model = "mistralai/Mistral-7B-Instruct-v0.2"
7 model = AutoModelForCausalLM . from_pretrained (
8 base_model ,
9 load_in_4bit = True ,
10 device_map = "auto"
11 )
12
13 # Load LoRA adapters
14 model = PeftModel . from_pretrained ( model , "your-username/siem-log-generator-mistral-7b-qlora" )
15 tokenizer = AutoTokenizer . from_pretrained ( "your-username/siem-log-generator-mistral-7b-qlora" )
16
17 # Generate security event analysis
18 prompt = "<s>[INST] event=network attack=DDoS [/INST]"
19 inputs = tokenizer ( prompt , return_tensors = "pt" ) . to ( "cuda" )
20 outputs = model . generate ( ** inputs , max_new_tokens = 100 )
21 print ( tokenizer . decode ( outputs [ 0 ] , skip_special_tokens = True ) )
Inference Example
1 # Analyze a security event
2 event = "timestamp=2024-01-14T10:30:00Z event=auth user=admin attack=BruteForce"
3 prompt = f"<s>[INST] { event } [/INST]"
4
5 inputs = tokenizer ( prompt , return_tensors = "pt" ) . to ( "cuda" )
6 with torch . no_grad ( ) :
7 outputs = model . generate (
8 ** inputs ,
9 max_new_tokens = 150 ,
10 temperature = 0.7 ,
11 top_p = 0.9 ,
12 do_sample = True
13 )
14
15 response = tokenizer . decode ( outputs [ 0 ] , skip_special_tokens = True )
16 print ( response )
Use Cases
1. Security Event Classification
Classify incoming logs into attack types or benign traffic.
2. MITRE ATT&CK Mapping
Automatically map security events to MITRE ATT&CK framework techniques.
3. Log Enrichment
Generate additional context and metadata for security events.
4. Threat Intelligence
Analyze patterns and generate threat reports from log data.
5. Training Data Generation
Create synthetic security logs for testing SIEM systems.
Limitations
Training Data : Model trained on limited samples (~500) for demonstrationDomain Specific : Optimized for SIEM/security logs, not general purposeLanguage : English onlyReal-time : Not optimized for ultra-low latency applicationsAccuracy : Should be used as an assistive tool, not sole decision-maker
Ethical Considerations
⚠️ Important Security Notice :
This model is for defensive cybersecurity purposes only
Do not use for malicious activities or unauthorized access
Always comply with applicable laws and regulations
Validate all model outputs before taking action
Use in conjunction with human security experts
Model Card Authors
Created by the SIEM Research Team
Citation
If you use this model in your research, please cite:
1 @misc{siem-log-generator-2025,
2 author = {Your Name},
3 title = {SIEM Log Generator - Mistral 7B QLoRA},
4 year = {2025},
5 publisher = {HuggingFace},
6 url = {https://huggingface.co/your-username/siem-log-generator-mistral-7b-qlora}
7 }
License
This model inherits the Apache 2.0 license from Mistral-7B-Instruct-v0.2.
Acknowledgments
Mistral AI for the base Mistral-7B-Instruct-v0.2 modelCICIDS2017 dataset contributorsHugging Face for the model hosting platformQLoRA paper authors for the efficient fine-tuning method
Contact
For questions or issues, please open an issue on the model repository.
Note : This is a research/demonstration model. For production SIEM deployments, additional training on larger, domain-specific datasets is recommended.
