🔍 Part 1: Fine-Tuning DeepSeek R1 1.5B on Synthetic Network Security Data

As part of a recent project exploring the use of open-source LLMs for cybersecurity, I’ve been working on fine-tuning DeepSeek R1 1.5B using synthetic data tailored for network traffic analysis.

Here’s a quick walkthrough of the approach:

🛠️ Environment Setup

We used a lightweight stack for efficiency:

• Model: deepseek-ai/deepseek-coder-1.5b-instruct
• Toolkit: Hugging Face transformers, datasets, trl, peft, and bitsandbytes
• Tuning Method: QLoRA for low-VRAM fine-tuning

📚 Dataset Preparation

We generated instruction-style JSONL data with prompts like:

cpp

<|user|> How do I detect DNS tunneling? 

<|assistant|> Look for long, suspicious subdomains and high-frequency queries.        

A dataset size of 500MB (~100K examples) gave us a solid base to train on domain-specific reasoning tasks (e.g., detecting anomalies in traffic patterns, simulating triage responses).

⚙️ Fine-Tuning Highlights

• LoRA config optimized for q_proj, v_proj
• Batch size = 1 with accumulation for low-memory setups
• Trained for 2–3 epochs on a single A10/V100 using mixed precision (fp16)

python

model = AutoModelForCausalLM.from_pretrained(model_name, load_in_4bit=True) model = prepare_model_for_kbit_training(model) model = get_peft_model(model, LoraConfig(...))
        

💡 Why It Matters Fine-tuning a compact model like DeepSeek R1 1.5B allows for:

• Domain-aware cybersecurity agents
• Efficient edge deployments
• Instruction-following for threat detection, log parsing, and response generation

✅ Stay tuned for Part 2: How I Generated the Synthetic Network Dataset (Labeled, Instructional, and Scalable to 500MB)

If you're working on LLM customization, threat detection, or LLMops, I’d love to hear how you're approaching this problem space!