farisalasmary / shieldrnn

The implementation of ShieldRNN

The Official ShieldRNN Implementation

This repository provides the official PyTorch implementation of the following paper:

ShieldRNN: A Distributed Flow-based DDoS Detection Solution For IoT Using Sequence Majority Voting

How to use?

The main script is train_shieldrnn.py which is used to train the ShieldRNN model. To extract features from a single PCAP file, use parse_data.py. If you have a large PCAP file, you may use run_multi_proc.sh that will automatically split the large PCAP file into multiple smaller PCAP files, extract features in parallel, and combine them in the right order.

You can use the provided recipes to train the ShieldRNN model on CIC-IDS2017 data:

• run_train_wed.sh to run the experiment of CIC-IDS2017-Wednesday.
• run_train_fri.sh to run the experiment of CIC-IDS2017-Friday.

you may also follow the same steps in the recipes to train ShieldRNN on your own data.

Authors

• Faris Alasmary - farisalasmary

License

This project is licensed under the MIT License - see the LICENSE file for details

Citation

@article{alasmary2022shieldrnn,
	author={Alasmary, Faris and Alraddadi, Sulaiman and Al-Ahmadi, Saad and Al-Muhtadi, Jalal},
	journal={IEEE Access},
	title={ShieldRNN: A Distributed Flow-Based DDoS Detection Solution for IoT Using Sequence Majority Voting},
	year={2022},
	volume={10},
	number={},
	pages={88263-88275},
	doi={10.1109/ACCESS.2022.3200477}
}