ENViSEC: AI-enabled Cybersecurity for Future Smart Environments:

In this study, we propose an approach with a framework to discover malware attacks on IoT devices using artificial intelligence (AI) enabled methods. The work belongs to the ENViSEC project which aims at developing top-notch solutions for bringing IoT security forward and offering similarity-based detection using AI models. For more details please visit, SmartSecLab and Kristiania University College.

Dataset used for attack detection in IoT devices

We have taken Aposemat IoT-23 and Edge-IIoTset datasets for training and testing the machine learning models. The IoT-23 dataset is semi-structured logging information of the packets labeled with malicious and benign IoT network traffic. The refined data is used for both training and predicting the attacks and malware in multi-agent systems.

Framework:

The building block of the proposed framework (Please refer our paper for detailed description):

Experiments:

The processed CSV data files are used for training and testing the models.

Preprocessing and training the model:

$ sh scripts/preprocess.sh | script to generate the refined version of the dataset from the original raw dataset.
$ python3 src/run.py | training one of the AI models, Deep Neural Networks, Support Vector Machine, Decision Tree, Random Forests, other shallow learning models on the dataset specified in config.yaml. \

Prediction of cyberattacks:

The light-weight version of the trained ML model is deployed to the IoT nodes to identify the malware and attacks on Smart Environment network connected to the access point.

$ python3 rpi/publish.py | to publish the processed running network traffic data on the localhost.
$ python3 rpi/predict.py | to predict the cyberattacks on the processed data in the gateways.

The following figure shows the AI model transfer and prediction approach.

Test-cases and middleware architecture:

The proposed middleware architecture with some hardware test-cases to replicate the real-world scenarios are presented in the firmware directory.

Citation:

Please cite this project work by referring to these papers:

Guru Bhandari, Andreas Lyth, Andrii Shalaginov and Tor-Morten Grønli, "Distributed Deep Neural-Network-Based Middleware for Cyber-Attacks Detection in Smart IoT Ecosystem: A Novel Framework and Performance Evaluation Approach", Electronics, 2023, 12(2), 298. https://doi.org/10.3390/electronics12020298.

@article{bhandari2023distributed,
title={Distributed Deep Neural-Network-Based Middleware for Cyber-Attacks Detection in Smart IoT Ecosystem: \
A Novel Framework and Performance Evaluation Approach},
author={Bhandari, Guru and Lyth, Andreas and Shalaginov, Andrii and Grønli, Tor-Morten},
journal={Electronics},
volume={12},
number={2},
pages={298},
doi={10.3390/electronics12020298}
year={2023},
publisher={MDPI}
}

Guru Prasad Bhandari, Andreas Lyth, Andrii Shalaginov and Tor-Morten Grønli, "Artificial Intelligence Enabled Middleware for Distributed Cyberattacks Detection in IoT-based Smart Environments", 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, pp. 3023-3032, https://doi.org/10.1109/BigData55660.2022.10020531.

@inproceedings{bhandari2022:artificial,
author={Bhandari, Guru Prasad and Lyth, Andreas and Shalaginov, Andrii and Grønli, Tor-Morten},
title={Artificial Intelligence Enabled Middleware for Distributed Cyberattacks Detection in IoT-based Smart Environments},
booktitle={2022 IEEE International Conference on Big Data (Big Data)},
year={2022},
pages={3023-3032},
publisher = {{IEEE}}
doi={10.1109/BigData55660.2022.10020531}
}

Acknowledgements:

This work is a part of the ENViSEC project which has received funding from the‌ European Union’s Horizon 2020 research and innovation program within the framework of the NGI POINTER Project funded under grant agreement# 871528.