Applied LLMs · Research prototype
Fine-Tuning LLMs for Network Security
An end-to-end experiment using QLoRA fine-tuning to generate structured synthetic network-attack data for downstream analysis and validation.
The challenge
Start with the product question.
Security datasets can be limited or imbalanced. The experiment asks whether a fine-tuned language model can generate structured examples while preserving meaningful attack characteristics.
What I built
A working system to learn from.
I assembled a pipeline covering dataset extraction, statistical analysis, feature selection, instruction design, training-data preparation, fine-tuning, generation, and conversion back to structured records for validation.
Product decisions
The thinking behind the build.
- 01
Begin with the downstream use case and validation path before choosing a model.
- 02
Use classical feature-selection methods to inform the generative task.
- 03
Constrain outputs with explicit structure so generated data can be parsed and checked.
- 04
Treat synthetic output as a hypothesis to validate—not automatically trustworthy data.
This is an independent project shared to demonstrate product thinking and hands-on exploration. It does not represent confidential employer work or commercial performance claims.