{"id":15682454,"url":"https://github.com/mohab-sameh/anomaly-based-ids-workbench","last_synced_at":"2025-09-08T03:37:36.578Z","repository":{"id":48953014,"uuid":"303173177","full_name":"mohab-sameh/Anomaly-Based-IDS-Workbench","owner":"mohab-sameh","description":"The ultimate workbench for research \u0026 development of AI-powered anomaly-based Intrusion Detection Systems (IDS)","archived":false,"fork":false,"pushed_at":"2022-07-05T16:06:16.000Z","size":77158,"stargazers_count":4,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-28T14:51:17.973Z","etag":null,"topics":["deep-learning","intrusion-detection","intrusion-detection-system","machine-learning","security"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"![OS](https://img.shields.io/badge/OS-Windows/Mac/Ubuntu-informational?style=flat\u0026logo=\u003cLOGO_NAME\u003e\u0026logoColor=white\u0026color=2bbc8a) ![Language](https://img.shields.io/badge/Language-Python-informational?style=flat\u0026logo=\u003cLOGO_NAME\u003e\u0026logoColor=white\u0026color=2bbc8a) ![IDE](https://img.shields.io/badge/IDE-VSCode-informational?style=flat\u0026logo=\u003cLOGO_NAME\u003e\u0026logoColor=white\u0026color=2bbc8a) ![Platform](https://img.shields.io/badge/Platform-Streamlit-informational?style=flat\u0026logo=\u003cLOGO_NAME\u003e\u0026logoColor=white\u0026color=2bbc8a) ![Models](https://img.shields.io/badge/Models-Sklearn/Tensorflow-informational?style=flat\u0026logo=\u003cLOGO_NAME\u003e\u0026logoColor=white\u0026color=2bbc8a)\n\n\u003ch1 align='center'\u003e Anomaly-Based Intrusion Detection Workbench 🔍\u003c/h1\u003e\n\n\u003cp align='center'\u003eThis is a workbench for the research and development of Anomaly-Based Intrusion Detection Systems\u003c/a\u003e.\u003c/p\u003e\n\u003cbr /\u003e\n\n\n\n\u003ch1 align='center'\u003e\u003ci\u003eDemo\u003c/i\u003e\u003c/h1\u003e\n\u003cimg src=\"https://github.com/mohab-sameh/Anomaly-Based-IDS-Workbench/blob/main/Implementation/app-files/Demo/demo.gif\" align=\"center\"\u003e\n\n\u003cbr /\u003e\n\n\n\u003ch1 align='center'\u003e\u003ci\u003eSome Features 📋\u003c/i\u003e\u003c/h1\u003e\n\n* Easily develop complete \u0026 usable machine learning and deep learning pipelines 🧠\n* Utilize 3rd Party Datasets (such as NSL-KDD, KDD-99, ISCX-NBXX) 📊\n* Connect and import CSV datasets through your AWS S3 buckets 🗃️\n* Perform Live Packet Capture \u0026 predict network attacks using your developed ML/DL Model! ☢️🔍\n* Export comparative Metrics of executed pipelines 📑\n* Simple and Intuitive GUI 🖥️\n* Cloud-Deployable ☁️\n* Tons of Data exploration, preprocessing, machine learning, and deep learning tools! 💻\n* Cross-Platform usability 💻📱🖥️\n\n\n\u003cbr /\u003e\n\n\u003ch1 align='center'\u003e\u003ci\u003eTested Platforms 🖥️\u003c/i\u003e\u003c/h1\u003e\n\n* Deployed on Windows 10 (20H2), Mac OS 10.14, Ubuntu 18.04/20.04\n* Access through any device with your browser of choice (tested on Firefox, Safari, MS Edge, Chrome, Opera).\n\n\n\n\n\u003cbr /\u003e\n\n\u003ch1 align='center'\u003e\u003ci\u003eInstallation 📜\u003c/i\u003e\u003c/h1\u003e\n\n* Install requirements:\n```\npip install requirements.txt\n```\n\n\n\n\n\u003cbr /\u003e\n\u003ch1 align='center'\u003e\u003ci\u003eUsage⌨️\u003c/i\u003e\u003c/h1\u003e\n\n* Run app:\n```\nstreamlit run app.py\n```\n* Use through your browser of choice. \n\n* Or Try a ready cloud-deployed instance [here](https://share.streamlit.io/mohab-sameh/anomaly-based-ids-workbench/main/Implementation/app-files/app.py)\n\n\n\n\n\u003cbr /\u003e\n\u003ch1 align='center'\u003e\u003ci\u003ePacket Capture Dependencies 🔍\u003c/i\u003e\u003c/h1\u003e\n\n* Libpcap:\n```\npip install libpcap-dev\n```\n* GCC ([installation instructions](https://linuxize.com/post/how-to-install-gcc-compiler-on-ubuntu-18-04/))\n* 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))\n\nnote: please make sure the KDD Feature extractor is in the root directory (ex: ~/Kdd99-Feature-Extractor-Prebuilt/kdd99_feature_extractor-master)\n\n\n\n\u003cbr /\u003e\n\n\n\n\u003ch2\u003e \n  Published literature:\n\u003c/h2\u003e\n\n[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)\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmohab-sameh%2Fanomaly-based-ids-workbench","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmohab-sameh%2Fanomaly-based-ids-workbench","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmohab-sameh%2Fanomaly-based-ids-workbench/lists"}