import streamlit as st import pandas as pd import subprocess import time import streamlit.components.v1 as components # ---------------------------- Header and Introduction ---------------------------- st.set_page_config(page_title="LLMs for Cyber Security", page_icon="🔒", layout="wide", initial_sidebar_state="expanded") st.title("🔒📊 LLMs for Cyber Security: State-of-the-Art Surveys📊🔒") st.markdown("This app is based on the paper: [Large Language Models for Cyber Security](https://arxiv.org/pdf/2405.04760v3). It showcases LLMs in the cybersecurity landscape, summarizing key surveys and insights.") st.markdown('🔒📊 https://arxiv.org/abs/2405.04760v3') # ---------------------------- Data Preparation ---------------------------- data = { "Reference": ["Motlagh et al.", "Divakaran et al.", "Yao et al.", "Yigit et al.", "Coelho et al.", "Novelli et al.", "LLM4Security"], "Year": [2024, 2024, 2023, 2024, 2024, 2024, 2024], "Scope": ["Security application", "Security application", "Security application, Security of LLM", "Security application, Security of LLM", "Security application", "Security application", "Security application"], "Dimensions": ["Task", "Task", "Model, Task", "Task", "Task, Domain specific technique", "Task, Model, Domain specific technique", "Model, Task, Domain specific technique, Data"], "Time frame": ["2022-2023", "2020-2024", "2019-2024", "2020-2024", "2021-2023", "2020-2024", "2020-2024"], "Papers": ["Not specified", "Not specified", 281, "Not specified", 19, "Not specified", 127] } df = pd.DataFrame(data) # ---------------------------- Display Data Table ---------------------------- st.subheader("📊 Survey Overview Table") st.dataframe(df, height=300) st.markdown("---") # ---------------------------- Mermaid Diagram Visualization ---------------------------- st.subheader("🛡️ Security Model Visualization with Mermaid") mermaid_code = ''' graph TD; A[LLMs in Security] --> B[Security Application] B --> C[Task] B --> D[Model] D --> E[Domain-Specific Techniques] E --> F[Data] ''' # HTML component for Mermaid diagram mermaid_html = f""" 
        {mermaid_code}
""" components.html(mermaid_html, height=300) st.markdown(""" Figure: The diagram illustrates how Large Language Models (LLMs) are applied in security, highlighting the flow from general applications to specific tasks, models, domain-specific techniques, and data considerations. """) st.markdown("---") # ---------------------------- Interactive Chart Example ---------------------------- st.subheader("📈 Interactive Chart Example") # Sample data for the chart chart_data = [ {"year": 2020, "papers": 50}, {"year": 2021, "papers": 80}, {"year": 2022, "papers": 120}, {"year": 2023, "papers": 200}, {"year": 2024, "papers": 250}, ] # HTML component for Chart.js chart_html = f""" """ components.html(chart_html, height=300) st.markdown("This interactive chart shows the growth in the number of papers on LLMs in cybersecurity over the years.") st.markdown("---") # ---------------------------- Interactive D3.js Visualization ---------------------------- st.subheader("🌐 Interactive D3.js Visualization") # Sample data for the D3 visualization d3_data = [ {"name": "Task", "value": 30}, {"name": "Model", "value": 25}, {"name": "Domain-Specific", "value": 20}, {"name": "Data", "value": 15}, {"name": "Security of LLM", "value": 10}, ] # HTML component for D3.js visualization d3_html = f"""
""" components.html(d3_html, height=300) st.markdown("This D3.js visualization shows the distribution of different aspects in LLM cybersecurity research.") st.markdown("---") # ---------------------------- Scrollable Content for Additional Insights ---------------------------- st.subheader("📝 Additional Insights") st.markdown("""

Survey Highlights:

Key Observations:

  1. The interest in applying LLMs to cybersecurity has significantly increased since 2019.
  2. There's a growing focus on not just using LLMs for security tasks but also securing the LLMs themselves.
  3. Domain-specific techniques are becoming more prominent, indicating a move towards specialized security solutions.
""", unsafe_allow_html=True) st.markdown("---") # ---------------------------- Security Audit Section ---------------------------- st.subheader("🔍 Run Python Dependency Security Audit") st.markdown("Keeping your project's dependencies secure is crucial. Use the button below to run a security audit on the Python packages used in this environment.") if st.button('Run pip-audit for Security Check'): with st.spinner('Running security audit...'): time.sleep(2) result = subprocess.run(['pip-audit'], capture_output=True, text=True) st.code(result.stdout) st.success('Security audit completed!') st.markdown("Note: The pip-audit tool checks your Python environment for packages with known vulnerabilities, referencing public CVE databases.") st.markdown("---") # ---------------------------- AI Pair Programming Recommendations ---------------------------- st.subheader("🤖 AI Pair Programming: Security Recommendations") st.markdown(""" Leveraging AI in pair programming can enhance code security and quality. Here are some recommendations: 1. **Reduce Code Complexity**: AI tools can suggest code refactoring to simplify complex code blocks, making them more maintainable and less error-prone. 2. **Minimize Attack Surface**: AI can identify unnecessary code paths and dependencies, allowing developers to remove or secure them. 3. **Automate Security Scans**: Integrate AI-powered security scanners to continuously monitor code for vulnerabilities. 4. **Code Review Assistance**: AI can assist in code reviews by highlighting potential security issues and non-compliance with best practices. 5. **Secure Coding Practices**: AI can provide real-time suggestions for secure coding patterns and discourage the use of insecure functions. """) st.markdown("---") # ---------------------------- Azure Deployment Information ---------------------------- st.subheader("☁️ Azure Deployment Information") st.markdown(""" While this demo does not include operational deployment, here's how you can deploy this application using Azure services: **Azure Container Apps**: Use Azure Container Apps to deploy and manage containerized applications at scale without managing infrastructure. - Benefits: - Serverless containers - Built-in support for scaling - Integrated with Azure services **Azure Container Registry (ACR)**: Store and manage your container images securely. - Steps: 1. Build your Docker image. 2. Push the image to ACR. 3. Configure Azure Container Apps to pull the image from ACR. **Azure Cosmos DB**: Use Cosmos DB to store security audit results, logs, and other application data. - Features: - Globally distributed - Multi-model database service - Low latency and high availability """) st.markdown("---") # ---------------------------- Footer and Additional Resources ---------------------------- st.subheader("📚 Additional Resources") st.markdown(""" - [Official Streamlit Documentation](https://docs.streamlit.io/) - [pip-audit GitHub Repository](https://github.com/pypa/pip-audit) - [Mermaid Live Editor](https://mermaid.live/) - Design and preview Mermaid diagrams. - [Azure Container Apps Documentation](https://docs.microsoft.com/en-us/azure/container-apps/) - [Cybersecurity Best Practices by CISA](https://www.cisa.gov/cybersecurity-best-practices) """) st.markdown("If you have any questions or would like to contribute to this project, please reach out or submit a pull request on GitHub.") # ---------------------------- Sidebar Content ---------------------------- st.sidebar.title("Navigation") st.sidebar.markdown(""" - [Introduction](#llms-for-cyber-security-state-of-the-art-surveys) - [Survey Overview Table](#survey-overview-table) - [Security Model Visualization](#security-model-visualization-with-mermaid) - [Interactive Chart](#interactive-chart-example) - [D3.js Visualization](#interactive-d3js-visualization) - [Additional Insights](#additional-insights) - [Security Audit](#run-python-dependency-security-audit) - [AI Recommendations](#ai-pair-programming-security-recommendations) - [Azure Deployment](#azure-deployment-information) - [Additional Resources](#additional-resources) """, unsafe_allow_html=True) st.sidebar.title("About") st.sidebar.info(""" This Streamlit app was developed to demonstrate the intersection of Large Language Models and Cybersecurity, highlighting recent surveys and providing tools and recommendations for secure coding practices. """) # ---------------------------- End of App ----------------------------