Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/mohab-sameh/attackbench
A workbench to simulate, research, and develop ML-powered Intrusion Detection Systems to prevent next-gen network attacks.
https://github.com/mohab-sameh/attackbench
firewall hacking hacking-tool intrusion-detection intrusion-prevention network-security networking python security streamlit
Last synced: 9 days ago
JSON representation
A workbench to simulate, research, and develop ML-powered Intrusion Detection Systems to prevent next-gen network attacks.
- Host: GitHub
- URL: https://github.com/mohab-sameh/attackbench
- Owner: mohab-sameh
- License: gpl-3.0
- Created: 2023-04-20T13:19:30.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-07-25T22:59:04.000Z (over 1 year ago)
- Last Synced: 2024-12-11T12:29:35.661Z (2 months ago)
- Topics: firewall, hacking, hacking-tool, intrusion-detection, intrusion-prevention, network-security, networking, python, security, streamlit
- Language: Python
- Homepage: https://attackbench.streamlit.app/
- Size: 27.7 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
    
AttackBench 🔍
AttackBench is a workbench for the research and development of Anomaly-Based Intrusion Detection Systems.
Quick Look 👀
Some Features 📋
* Easily develop complete & usable machine learning and deep learning pipelines 🧠
* Utilize 3rd Party Datasets (such as NSL-KDD, KDD-99, ISCX-NBXX) 📊
* Connect and import CSV datasets through your AWS S3 buckets 🗃️
* 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 💻📱🖥️
Demo
Want to see AttackBench in action?
Tested Platforms 🖥️
* 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).
Installation 📜
* Install requirements:
```
pip install requirements.txt
```
Usage⌨️
* Run app:
```
streamlit run app.py
```
* Use through your browser of choice.
* Or Try a ready cloud-deployed instance [here]([https://share.streamlit.io/mohab-sameh/anomaly-based-ids-workbench/main/Implementation/app-files/app.py](https://attackbench.streamlit.app/))
Packet Capture Dependencies 🔍
* Libpcap:
```
pip install libpcap-dev
```
* GCC ([installation instructions](https://linuxize.com/post/how-to-install-gcc-compiler-on-ubuntu-18-04/))
* KDD Feature extractor ([repo](https://github.com/AI-IDS/kdd99_feature_extractor) or use my [prebuilt repo](https://github.com/mohab-sameh/Kdd99-Feature-Extractor-Prebuilt))
> Note: please make sure the KDD Feature extractor is in the root directory (ex: ~/Kdd99-Feature-Extractor-Prebuilt/kdd99_feature_extractor-master)
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)