N-layer Model Web Application - https://n-layer.uc.r.appspot.com/

This repository contains the code for the N-layer model web application built with Flask. The N-layer model is a tool for analyzing and optimizing multi-layered security systems. The application provides two main types of analysis: Probabilistic Model and Strategic Model, allowing the user to configure the number of layers and associated costs.

Table of Contents

• Installation
• Usage
  • Running the Web Application
  • Using the N-layer Model
  • Models and Configuration
  • Result Visualization
• Code Structure
• Layer Images

---

Installation

To set up the project, you need to have Python 3.x installed on your system. Follow these steps to get started:

1. Clone the repository:

   git clone https://github.com/mohanagolleru/N_layer.git
   cd N_layer
   

2. Install the required dependencies:

   pip install -r requirements.txt

3. Start the Flask server:

   python app.py

---

Usage

Running the Web Application

Once the Flask server is running, open your web browser and navigate to the following address:

http://127.0.0.1:5000/

This will take you to the N-layer model web interface, where you can interact with the application. You will be able to configure the model, select different options, and view the results in an intuitive interface.

Steps to Use the N-layer Model:

1. Select a Model Type:

   • Probabilistic Model: This model evaluates systems facing non-strategic attackers.
   • Strategic Model: This model incorporates game theory to analyze systems dealing with strategic attackers.

2. Configure the Model:

   • Use the slider to set the Total Layers. The number of layers represents the number of security layers in the system.
   • Use the slider to set the Cost, which defines the total available budget for securing the layers.

3. Generate the Solution: Once you've configured the parameters, click the Solve button to generate the optimal solution based on your inputs. The results, including a stacked plot showing budget allocation, will be displayed on the results page.

---

Models and Configuration

Probabilistic Model

This model is designed to analyze systems that are exposed to non-strategic attackers. It calculates the probabilistic nature of defense across multiple layers based on system characteristics such as cost, risk, and attacker behavior.

Strategic Model

The strategic model involves game-theoretic concepts to model interactions between attackers and defenders. It is useful in systems where attackers behave strategically to exploit vulnerabilities in the defense system.

---

Result Visualization

Once the model is solved, the application generates a stacked plot showing the optimal budget allocation across the configured layers. You can also view the layer-specific configuration and results in a table format.

---

Optimization Outputs

Displays the model type, solutions, and parameters such as s, beta, alpha, theta, gamma, cost, and C_bar.

---

Code Structure

.
├── app.py                 # Main application file (Flask server)
├── requirements.txt       # List of Python dependencies
├── static/                # Static files (e.g., CSS, images)
│   ├── css/               # CSS files for styling
│   │   └── styles.css     # Main stylesheet for the web application
│   └── images/            # Image assets
│       └── logo.png       # Example image file
├── templates/             # HTML templates for Flask
│   ├── base.html          # Base template with common HTML structure
│   ├── index.html         # Home page template with form input
│   ├── results.html       # Page template to display results and plots
│   └── about.html         # About page template
├── models/                # Directory for model-related Python code
│   ├── probabilistic.py   # Code for the probabilistic model
│   └── strategic.py       # Code for the strategic model 
├── utils/                 # Utility scripts for helper functions
│   └── visualization.py   # Helper functions for result visualization
├── data/                  # Directory for storing input data or pre-configured data
│   └── example_data.csv   # Example CSV file for testing
├── tests/                 # Directory for test scripts (optional)
│   └── test_models.py     # Test cases for the model functionality
├── README.md              # Project documentation 
└── LICENSE                # License for the project

• app.py: This file acts as the central hub of the application, defining all routes for handling form submissions, managing optimization logic, and generating results for the user.
• templates/: Contains HTML templates that render the interface for the user. These include the form for input data, result display, and other pages like the About page.
• static/: Holds static assets such as CSS files for styling and images used within the web application (e.g., logos or graphical assets).
• models/: Contains Python scripts for implementing the core logic of the probabilistic and strategic models. These files are responsible for running the calculations for each model type.
• utils/: Utility functions, including scripts to handle result visualization and other helper tasks.
• data/: Placeholder for storing input data or any pre-configured datasets.
• tests/: Contains test cases to verify the accuracy and functionality of the models and utility functions.

---

Layer Images

Below are the visual representations for layers 1 to 4:

Layer 1

Layer 2

Layer 3

Layer 4