AutoAudit - Improve Network Security with a Specialized Large Language Model Tailored for Precise Auditing

AutoAudit: An Overview of Innovation in Cybersecurity

AutoAudit represents a significant leap forward in the field of cybersecurity by focusing on the development and application of specific-domain large language models. This project addresses the conspicuous gap in the availability of specialized language models designed for network security, similar to the approach seen in other verticals like healthcare, finance, and law.

Introducing AutoAudit

AutoAudit is an innovative open-source initiative that leverages advanced natural language processing techniques to assist in security auditing and network defense. It offers capabilities such as malicious code analysis, network attack detection, and security vulnerability forecasting, all designed to bolster the efforts of security professionals in real-time.

With its potential to become an invaluable tool for security operations, AutoAudit aims to deliver accurate and rapid analyses and predictions, aiding in the battle against ever-evolving cyber threats. Microsoft Security Copilot, a similar initiative by Microsoft, suggests that this is a promising direction for integrating language model capacities in cybersecurity.

The AutoAudit Models

AutoAudit currently includes several versions of its models:

AutoAudit-7B: A demo version crafted with the Alpaca-Lora framework, which shows commendable results in English-language network security contexts. However, it lacks contextual understanding, indicating that further development with more complex models is necessary.
AutoAudit-33B: This version is under internal testing, with intentions to release it after adequate refinements.

Deployment and Installation

To deploy AutoAudit, users must undertake several steps:

Download the repository: Clone the AutoAudit repository onto a local or remote server.
```
git clone [email protected]:ddzipp/AutoAudit.git
cd AutoAudit
```
Set up the environment: Create and activate a new Conda environment using Python 3.8.
```
conda create --name AutoAudit python=3.8
conda activate AutoAudit
```
Install dependencies: Use pip to install the required packages listed in the repository's requirements file.
```
pip install -r requirements.txt
```
Integrate ClamAV: Ensure ClamAV is installed and added to the system path.
Configure model weights: Specify paths for the Llama model and Lora weights which are fundamental for the model's enhancement.
Run the project: Start the server using the provided command to commence operations.
```
python manage.py runserver
```

Future Directions

AutoAudit's development roadmap includes significant ambitions:

Enhancing Logical Reasoning: Efforts are underway to refine the model's reasoning capabilities by training it on more substantial frameworks like ChatGLM or LLaMA2.
Improving Accuracy: Accuracy and reliability remain paramount. The team seeks to overcome existing limitations by expanding and refining the dataset, alongside improving methodologies for better professional outcomes.
Broadening Tool Integration: There's a desire to couple AutoAudit with more security tools and cover more security scenarios, such as automated vulnerability discovery and binary reverse engineering.
Linking to Real-World Data: By integrating Langchain, the aim is to enable the models to interact with external data sources.

Data Utilization

The AutoAudit project uses a combination of manual and self-generated data through a method called Self-Instruct. This technique helps in creating datasets compiled from reputable sources such as GitHub and Kaggle. These datasets follow a structured format aimed at delivering comprehensive security insights.

By simplifying the process of automatic dataset creation, those interested can utilize this transformative model to generate network security QA datasets compatible with base models like LLaMA (Alpaca).

Conclusion

AutoAudit emerges as a pioneering tool in the cybersecurity landscape, positioning itself as an assistant to security professionals by providing quintessential analysis capabilities. Its ongoing development promises to address the dynamic nature of cyber threats, ushering in an era of more robust protective strategies anchored by advanced natural language processing technologies.