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Cybersecurity Risk Detector is a machine learning-based web app that detects cybersecurity threats. It generates a custom dataset, tests multiple ML models, and integrates the best one into a Streamlit app for real-time risk prediction. Built with Python, Scikit-learn, and Pandas.

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Cybersecurity Risk Detector

Overview

This project was developed as part of a machine learning assignment to build a Cybersecurity Risk Detector. The aim was to classify and detect potential cybersecurity threats using custom-generated datasets and machine learning algorithms. The project culminates in a local web application for real-time risk prediction.

Project Requirements and Implementation

Problem Selection

The project tackles a classification problem to determine the presence or absence of cybersecurity risks. The dataset was custom-generated using Python and saved as veri.csv.

Implementation Steps

  1. Dataset Creation:
    • Generated synthetic data (veri.csv) using Python and stored the code in veri.ipynb.
  2. Model Training:
    • Trained the dataset using 10 different machine learning algorithms.
    • Selected the best-performing algorithm based on Accuracy.
    • Saved the best model as eniyi.joblib.
  3. Web Application Development:
    • Designed a local web application using Streamlit.
    • Integrated the trained model (eniyi.joblib) for real-time predictions.
  4. Project Demonstration:
    • A demonstration video link showcasing the working application is provided in link.text.

Features

  • Custom dataset generation.
  • Evaluation of multiple ML algorithms.
  • Streamlit-based web application for risk detection.
  • Best model saved for reuse.

Technologies Used

  • Programming Language: Python
  • Libraries:
    • Scikit-learn (ML algorithms)
    • Pandas, NumPy (data processing)
    • Matplotlib (visualization)
    • Streamlit (web application)
  • Model Serialization: joblib
  • Version Control: Git

Installation

  1. Clone the repository:
    git clone https://github.com/dirshaye/Cybersecurity_Risk_Detector.git
  2. Navigate to the project directory:
    cd dirshaye-cyber_security_risk_detector
  3. Install the dependencies:
    pip install -r requirements.txt

Usage

  1. Ensure the dataset (veri.csv) is in the root directory.
  2. To train models and select the best one:
    python app.py
  3. Launch the web application:
    streamlit run app.py
  4. Access the application in your browser and test predictions.

Project Structure

└── dirshaye-cyber_security_risk_detector/
    ├── algoritma.ipynb     # Notebook for evaluating algorithms
    ├── app.py              # Streamlit application script
    ├── eniyi.joblib        # Best-performing ML model
    ├── link.text           # Video demonstration link
    ├── veri.csv            # Generated dataset
    └── veri.ipynb          # Notebook for dataset generation

License

This project is licensed under the MIT License.

Contact

For any questions or feedback, feel free to reach out:

About

Cybersecurity Risk Detector is a machine learning-based web app that detects cybersecurity threats. It generates a custom dataset, tests multiple ML models, and integrates the best one into a Streamlit app for real-time risk prediction. Built with Python, Scikit-learn, and Pandas.

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