├── README.md
├── .idea
│ ├── .gitignore
│ ├── IoT-MiniProject.iml
│ ├── misc.xml
│ ├── modules.xml
│ ├── vcs.xml
│ └── inspectionProfiles
│ └── profiles_settings.xml
├── EDA # EDA, NAS and Self-Organising Maps
│ ├── bilinear.py
│ ├── features.py
│ ├── nas.py
│ └── som.py
├── Model_A # Base model #1
│ └── model.py
├── Model B # Base model #2
│ └── model.py
├── colab # Playground for notebooks
│ ├── IOT_IntrusionDetection_EDA_BaseModels.ipynb
│ └── Main_Model_DeepAE.ipynb
├── docs # Deploy docs
│ ├── _config.yml
│ ├── index.md
│ └── assets
│ ├── Figure_1.png
│ ├── IoTPipeline.png
│ ├── IoT_Botnet_models.png
│ ├── IoT_Botnet_models_new.png
│ ├── IoT_Modelkey.png
│ ├── abcd.png
│ ├── conf_C.png
│ ├── conf_D.png
│ ├── hardware.png
│ ├── hardware_mirai.png
│ ├── repo_size.png
│ ├── repo_time.png
│ ├── size_plots.png
│ ├── time_plots.png
│ └── r.jpeg
├── documentation # Documentation of Project
│ ├── IoT Mini Project - Proposal.pdf
│ ├── IoT_Botnet_models.png
│ ├── IoT_Modelkey.png
│ ├── model2.png
│ └── nn.svg
├── Model_D # Proposed model - Attack Classifier
│ ├── test.py
│ └── train.py
├── hardware # Botnet Server and Socket Connections
│ ├── arduino.ino
│ ├── deepLearningCheck.py
│ └── mirai.py
└── Model_C # Proposed model - Anomaly Detector
├── test.py
└── train.py
We provide an easy-to-get-started demo using Google Colab!
This will allow you to train a basic version of our method using GPUs on Google Colab.
| Description | Link |
|---|---|
| EDA Demo and Base Model Interactive Notebook |  |
@misc{Dua:2019 ,
author = "Dua, Dheeru and Graff, Casey",
year = "2017",
title = "{UCI} Machine Learning Repository",
url = "http://archive.ics.uci.edu/ml",
institution = "University of California, Irvine, School of Information and Computer Sciences" }@ARTICLE{8490192,
author={Meidan, Yair and Bohadana, Michael and Mathov, Yael and Mirsky, Yisroel and Shabtai, Asaf and Breitenbacher, Dominik and Elovici, Yuval},
journal={IEEE Pervasive Computing},
title={N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders},
year={2018},
volume={17},
number={3},
pages={12-22},
doi={10.1109/MPRV.2018.03367731}}
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