Summary of Attackqa: Development and Adoption Of a Dataset For Assisting Cybersecurity Operations Using Fine-tuned and Open-source Llms, by Varun Badrinath Krishna
AttackQA: Development and Adoption of a Dataset for Assisting Cybersecurity Operations using Fine-tuned and Open-Source LLMs
by Varun Badrinath Krishna
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A medium-difficulty summary: This paper develops a novel question-answering (Q&A) system for cybersecurity analysts in security operations centers. The system employs retrieval-augmented generation (RAG) on the AttackQA dataset, which comprises 25,335 Q&A pairs with rationales for fine-tuning and evaluation. The dataset was generated using a lightweight open-source language model (LLama 3 8B), which produced over 1,100 tokens per second with full 16-bit precision on SambaNova System’s SN40L hardware. To ensure quality, the paper fine-tuned LLama 3 70B to detect and reject low-quality Q&A pairs. The authors demonstrate that fine-tuning open-source embeddings and language models can yield superior accuracy compared to proprietary models like GPT-4o. Furthermore, the paper introduces a fully open-source, high-speed RAG and evaluation pipeline with a benchmark for model accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A low-difficulty summary: This research creates a new way for computers to answer questions about cybersecurity. The system uses a special dataset called AttackQA that contains over 25,000 questions and answers related to cybersecurity. The data was generated using a computer program that can produce text quickly and accurately. To make sure the data is good quality, the researchers used another computer program to review it and remove any bad information. The results show that this system can be more accurate than other similar systems that use proprietary technology. |
Keywords
* Artificial intelligence * Fine tuning * Gpt * Language model * Llama * Precision * Question answering * Rag * Retrieval augmented generation
