cyber-security-resources/ai_research

AI Security Research Resources

Langchain Resources

• Tools, Learning, Notebooks, Bots, Agent examples, etc.

AI Security Best Practices and Tools

• High-Level AI Security Best Practices
• Homomorphic-Encryption
• AI Security Tools and Frameworks
• AI Secure Deployment Tips
• AI Secure Design Tips
• Threat Modeling

AI Security Resources from Omar's Training Sessions

• Cybersecurity Learning Prompts
• Networking Prompts
• Programming Learning Prompts

AI Ethics and Privacy Resources

• AI Ethics and Privacy Resources

Tools & Methods for Data Collection, Cleaning, and Analysis:

• Data Collection:
  • APIs and SDKs
  • Wireless transmission

Data Cleaning:

3. Pandas:

   • Example: Cleaning a dataset with missing values using Pandas before training a machine learning model.
   • Relevant Link: Pandas Documentation
   • Usage in HAR and AI: Pandas can be used to structure and clean sensor data, making it suitable for training AI models capable of recognizing complex patterns in human activity data.

4. Sci-kit learn:

   • Example: Using Sci-kit learn for feature selection and removing irrelevant features from a dataset.
   • Relevant Link: Sci-kit learn Documentation
   • Usage in HAR and AI: Sci-kit learn offers various tools for data preprocessing, which is a vital step in preparing data for AI algorithms, enhancing the performance of the models in HAR applications.

Data Analysis:

5. TensorFlow:

   • Example: Developing a deep learning model using TensorFlow to classify different activities based on sensor data.
   • Relevant Link: TensorFlow Documentation
   • Usage in HAR and AI: TensorFlow provides a comprehensive platform for developing and training AI models capable of analyzing and recognizing patterns in human activity data.

6. Keras:

   • Example: Using Keras to create a convolutional neural network (CNN) for image recognition, an essential task in AI.
   • Relevant Link: Keras Documentation
   • Usage in HAR and AI: Keras simplifies the process of building and optimizing neural networks, a crucial component in AI, to analyze human activity data more effectively and make predictions.

Visualization and Further Analysis:

7. Matplotlib:

   • Example: Using Matplotlib to visualize the distribution of different activities within a dataset.
   • Relevant Link: Matplotlib Documentation
   • Usage in HAR and AI: Visualization of data is essential in AI to understand underlying patterns and trends in data, aiding in the better development and tuning of models for HAR.

8. Seaborn:

   • Example: Creating a heatmap using Seaborn to visualize the correlation between different features in a dataset.
   • Relevant Link: Seaborn Documentation
   • Usage in HAR and AI: Seaborn can enhance data visualization in AI, assisting in identifying relationships and patterns in data which can influence the development and performance of HAR models.