In this paper, we explore transferability in learning between different attack classes in a network intrusion detection setup. We evaluate transferability of attack classes by training a deep learning model with a specific attack class and testing it on a separate attack class. We observe the effects of real and synthetically generated data augmentation techniques on transferability. We investigate the nature of observed transferability relationships, which can be either symmetric or asymmetric. We also examine explainability of the transferability relationships using the recursive feature elimination algorithm. We study data preprocessing techniques to boost model performance. The code for this work can be found at https://github.com/ghosh64/transferability.
@article{arxiv.2312.11550,
title = {A Study on Transferability of Deep Learning Models for Network Intrusion Detection},
author = {Shreya Ghosh and Abu Shafin Mohammad Mahdee Jameel and Aly El Gamal},
journal= {arXiv preprint arXiv:2312.11550},
year = {2023}
}
Comments
A significantly revised version of this manuscript has been accepted for publication. This is a previous version of the manuscript containing results and discussions that could not be included in the accepted version