Siamese Network Usage for Learning from Small Dataset and for Zero-day attack detection

This repository builds, trains and tests Siamese Network model. The pairs and trained models are added to the repository for reproducibility.

Citation

To cite this code, please use the following papers;

@article{hindy2020leveraging,
  title={Leveraging Siamese Networks for One-Shot Intrusion Detection Model},
  author={Hindy, Hanan and Tachtatzis, Christos and Atkinson, Robert and Brosset, David and Bures, Miroslav and Andonovic, Ivan and Michie, Craig and Bellekens, Xavier},
  journal={arXiv preprint arXiv:2006.15343},
  year={2020}
}

@inproceedings{10.1145/3437984.3458842,
author = {Hindy, Hanan and Tachtatzis, Christos and Atkinson, Robert and Bayne, Ethan and Bellekens, Xavier},
title = {Developing a Siamese Network for Intrusion Detection Systems},
year = {2021},
isbn = {9781450382984},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3437984.3458842},
doi = {10.1145/3437984.3458842},
booktitle = {Proceedings of the 1st Workshop on Machine Learning and Systems},
pages = {120–126},
numpages = {7},
keywords = {Few-Shot Learning, Artificial Neural Network, Machine Learning, NSL-KDD, Intrusion Detection, CICIDS2017, Siamese Network, KDD Cup'99},
location = {Online, United Kingdom},
series = {EuroMLSys '21}
}

Usage Scenarios

TBU

Used Datasets:

CICIDS2017 here
kddcup.data_10_percent here
NSL-KDD here
SCADA here

All processed files can be downloaded from here

All pairs can be downloaded from here

To run the script with the default parameters:

To run (remember to add the parameters as specified in the below tables as needed):

CICIDS2017: python main.py --path {} --dataset_name CICIDS
kdd: python main.py --path {} --dataset_name kdd
nsl: python main.py --path {} --dataset_name nsl-kdd
SCADA: python main.py --path {} --dataset_name SCADA

Script Arguments

General Parameters

Argument	Usage	Default	Values and Notes
--verbose	If true, prints will be displayed	True
--path	Path of the dataset files / directory
--dataset_name	Specify the dataset name	kdd	CICIDS kdd nsl-kdd SCADA
--batch_size	Number of randomly generated pairs for training	30000
--testing_batch_size	Number of randomly generated pairs for testing	30000
--niterations	Number of training iterations	1000
--evaluate_every	Interval for testing	500
--output	The output file name	Result.csv
--network_path	The path of trained network (used to test a trained network and not train from scratch)

One-Shot related arguments