M.T.D. (Multi-Threat Detection):

Authors: Leo Martinez III, John Cavazos, Anthony Martinez, Jalen Williams

Contact: Leo: leo.martinez@students.tamuk.edu, Anthony: anos.martinez24@gmail.com

Created: Fall 2024 - Spring 2024

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Note:

Please see necessary dependencies at the top of each script if you choose to run it locally; some (not all) of the major dependencies are Scikit-Learn, TensorFlow, Keras, XGBoost, NumPy, and Pandas.

• The machine learning models were developed in Spyder IDE using Python 3.18.
• The Android app was developed in AndroidStudio using Java/XML.
• The website was developed in VS Code using HTML/CSS.

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Overview:

M.T.D. (Multi-Threat Detection) is a comprehensive cybersecurity solution designed to combat the ever-evolving landscape of cyber threats. The project employs a holistic approach, addressing common vulnerabilities with a focus on real-world relevance and practicality. A full detailed report for those interested in the specific details of the design for each machine learning model.

Features:

• Machine Learning Models: Four ML models are included, saved individually in folders:
  • Network Intrusion Detection
  • Network Intrusion Classification
  • Malware Detection
  • Malware Classification
• Scoring Scripts: Pickle (.pk1) files with scoring scripts allow customization for specific needs.
• Android App: A mobile Android app is provided to utilize the models and empower users to be more aware of cybersecurity threats.
• Website: An accompanying website offers easy access to information and the app.

Testing and Evaluation:

Each ML model has been rigorously tested against various research papers to prove its effectiveness. The project integrates diverse datasets to ensure solutions resonate with real-world scenarios. Various algorithms, including Random Forest, Support Vector Machines, and Artificial Neural Networks, are meticulously assessed for effectiveness and robustness.

Conclusion:

In conclusion, M.T.D. represents a proactive defense against cyber threats, underpinned by cutting-edge technology and a commitment to ongoing enhancement. As we navigate the complexities of the digital landscape, our project stands as a beacon of resilience and innovation in the fight for cybersecurity.

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Repository Structure:

• android_app: Folder containing source code and necessary files for the Android App.
• images: Contains images for visualization purposes.
• machine_learning_models: Contains subfolders for each ML task written in Python3.
• website: Contains necessary files for viewing the website written in HTML/CSS.
• MTD_Detailed_Report.docx: Highly detailed report for those interested in intricate details.
• README.md: Contains context information about the project (you are here!).
• LICENSE: Contains license information (MIT) for the GitHub repository.