Update to use HDC, update readme

README.md

@@ -1,12 +1,13 @@
 # PacketFlowAI
-PacketFlowAI is a sophisticated tool powered by deep learning, specifically designed for the real-time classification of network packets using Convolutional Neural Networks (CNN). By integrating the robust capabilities of PyTorch for model development and Scapy for packet capture and processing, PacketFlowAI is a highly efficient solution for discerning various types of network traffic.
+PacketFlowAI is a cutting-edge network security tool that uses Hyperdimensional Computing (HDC) to classify network packets in real-time. By transitioning from traditional Convolutional Neural Networks (CNN) to hyperdimensional computing, PacketFlowAI delivers improved robustness, scalability, and efficiency for packet classification. It leverages PyTorch for model development and Scapy for real-time packet capture and processing, providing a powerful solution for detecting and classifying various types of network traffic.
 
 ## Features
-- Real-time packet capture and classification
-- Custom CNN architecture for packet feature analysis
-- Training and evaluation on a labeled dataset
-- Normalization and scaling of packet features for optimal performance
-- Easy-to-use command-line interface for training and live packet capture modes
+- Real-time packet capture and classification with hyperdimensional computing
+- Hypervector-based data representation for robust and noise-tolerant classification
+- Customizable model architecture designed for high-dimensional input
+- Training and evaluation on labeled datasets using hyperdimensional techniques
+- Flexible command-line interface for training and live packet capture modes
+- Scalability and adaptability to new features and network threats
 
 ## Installation
 To set up PacketFlowAI, follow these steps:
@@ -34,25 +35,35 @@ To train the model on your dataset, use the following command:
 python main.py --mode train
 ```
 
-This will train the model using the dataset specified in the code and save the best-performing model for future use.
+This command will preprocess the dataset, convert the packet features and textual data into hypervectors, and train the model using hyperdimensional techniques. The best-performing model is saved for future use.
 
 ### Live Capture Mode
 For on-the-fly classification of network traffic, enter the following:
 
 ```bash
 python main.py --mode capture [--interface <interface_name>]
 ```
-The --interface flag is optional and allows you to specify the network interface for packet capture. If not provided, it defaults to 'eth0'.
+The --interface flag allows you to specify the network interface for packet capture (e.g., eth0 or wlan0). If not provided, it defaults to eth0.
 
-Note: Ensure you possess the requisite permissions for packet capture on the chosen network interface.
+Ensure you have the necessary permissions for capturing packets on the chosen interface.
+
+PacketFlowAI will capture packets, convert their features into hypervectors, and classify them using the trained model.
 
 ## Customization
 
-PacketFlowAI offers flexibility allowing you to tailor the CNN architecture, dataset, and training parameters. Simply adjust the settings in the main.py script to align with your specific requirements.
+PacketFlowAI provides flexibility, allowing you to customize the model architecture, dataset, and hyperdimensional parameters. You can adjust the settings in the main.py script to suit your specific use case, including:
+
+- Modifying the hypervector dimensions and encoding methods.
+- Altering the model architecture (e.g., number of layers, dropout rates).
+- Adjusting training parameters (e.g., batch size, learning rate, number of epochs).
+
+## Key Concepts in PacketFlowAI
+- Hyperdimensional Computing: A novel approach where data is represented as high-dimensional vectors (hypervectors), which offers increased robustness to noise and scalability in complex environments.
+- Binding and Permutation: Core operations in hyperdimensional computing that encode associations between data and capture sequential information.
 
 ## Contributing
 
-Contributions to PacketFlowAI are welcome! Please feel free to submit pull requests or open issues to suggest improvements or add new features.
+Contributions to PacketFlowAI are encouraged! If you have ideas for improvement, new features, or bug fixes, please submit a pull request or open an issue. Together, we can make PacketFlowAI more robust and feature-rich.
 
 ## License