Federated Learning for XSS Detection: A Privacy-Preserving Approach

Mahran Jazi, Irad Ben-Gal

2024

Abstract

Collaboration between edge devices has increased the scale of machine learning (ML), which can be attributed to increased access to large volumes of data. Nevertheless, traditional ML models face significant hurdles in securing sensitive information due to rising concerns about data privacy. As a result, federated learning (FL) has emerged as another way to enable devices to learn from each other without exposing user’s data. This paper suggests that FL can be used as a validation mechanism for finding and blocking malicious attacks such as cross-site scripting (XSS). Our contribution lies in demonstrating the practical effectiveness of this approach on a real-world dataset, the details of which are expounded upon herein. Moreover, we conduct comparative performance analysis, pitting our FL approach against traditional centralized parametric ML methods, such as logistic regression (LR), deep neural networks (DNNs), support vector machines (SVMs), and k-nearest neighbors (KNN), thus shedding light on its potential advantages. The dataset employed in our experiments mirrors real-world conditions, facilitating a meaningful assessment of the viability of our approach. Our empirical evaluations reveal that the FL approach not only achieves performance on par with that of centralized ML models but also provides a crucial advantage in terms of preserving the privacy of sensitive data.

Paper Citation

in Harvard Style

Jazi M. and Ben-Gal I. (2024). Federated Learning for XSS Detection: A Privacy-Preserving Approach. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-716-0, SciTePress, pages 283-293. DOI: 10.5220/0012921800003838

in Bibtex Style

@conference{kdir24,
author={Mahran Jazi and Irad Ben-Gal},
title={Federated Learning for XSS Detection: A Privacy-Preserving Approach},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2024},
pages={283-293},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012921800003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Federated Learning for XSS Detection: A Privacy-Preserving Approach
SN - 978-989-758-716-0
AU - Jazi M.
AU - Ben-Gal I.
PY - 2024
SP - 283
EP - 293
DO - 10.5220/0012921800003838
PB - SciTePress