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README.md

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-# GradProject
+![OS](https://img.shields.io/badge/OS-Windows/Mac/Ubuntu-informational?style=flat&logo=<LOGO_NAME>&logoColor=white&color=2bbc8a) ![Language](https://img.shields.io/badge/Language-Python-informational?style=flat&logo=<LOGO_NAME>&logoColor=white&color=2bbc8a) ![IDE](https://img.shields.io/badge/IDE-VSCode-informational?style=flat&logo=<LOGO_NAME>&logoColor=white&color=2bbc8a) ![Platform](https://img.shields.io/badge/Platform-Streamlit-informational?style=flat&logo=<LOGO_NAME>&logoColor=white&color=2bbc8a) ![Models](https://img.shields.io/badge/Models-Sklearn/Tensorflow-informational?style=flat&logo=<LOGO_NAME>&logoColor=white&color=2bbc8a)
+
+<h1 align='center'> Anomaly-Based Intrusion Detection Workbench 🔍</h1>
+
+<p align='center'>This is a workbench for the research and development of Anomaly-Based Intrusion Detection Systems</a>.</p>
+
+
+
+
+<h1 align='center'><i>Demo</i></h1>
+
+
+<h1 align='center'><i>Some Features 📋</i></h1>
+
+* Easily develop complete & usable machine learning and deep learning pipelines 🧠
+* Utilize 3rd Party Datasets (such as NSL-KDD, KDD-99, ISCX-NBXX) 📊
+* Perform Live Packet Capture & predict network attacks using your developed ML/DL Model! ☢️🔍
+* Export comparative Metrics of executed pipelines 📑
+* Simple and Intuitive GUI 🖥️
+* Cloud-Deployable ☁️
+* Tons of Data exploration, preprocessing, machine learning, and deep learning tools! 💻
+* Cross-Platform usability 💻📱🖥️
+
+<h1 align='center'><i>Tested Platforms 🖥️</i></h1>
+
+* Deployed on Windows 10 (20H2), Mac OS 10.14, Ubuntu 18.04/20.04
+* Access through any device with your browser of choice (tested on Firefox, Safari, MS Edge, Chrome, Opera).
+
+
+
+<h1 align='center'><i>Installation 📜</i></h1>
+
+* Install requirements:
+```
+pip install requirements.txt
+```
+
+<h1 align='center'><i>Usage⌨️</i></h1>
+
+* Run app:
+```
+streamlit run app.py
+```
+* Use through your browser of choice. 
+
+
+
+
+
+## Published literature:
+* [M. S. Abdel-Wahab, A. M. Neil and A. Atia, "A Comparative Study of Machine Learning and Deep Learning in Network Anomaly-Based Intrusion Detection Systems," 2020 15th International Conference on Computer Engineering and Systems (ICCES), 2020, pp. 1-6, doi: 10.1109/ICCES51560.2020.9334553.](https://ieeexplore.ieee.org/document/9334553)
+
+
+