An Approach for Privacy-Preserving Mobile Malware Detection Through Federated Machine Learning

Giovanni Ciaramella, Giovanni Ciaramella, Fabio Martinelli, Francesco Mercaldo, Francesco Mercaldo, Christian Peluso, Antonella Santone

2024

Abstract

Considering the diffusion of smart devices and IoT devices, mobile malware detection represents a task of fundamental importance, considering the inefficacy of signature-based antimalware free and commercial software, which can detect a threat only if its signature is present in the antimalware repository. In the last few years, many methods have been proposed by academia to identify so-called zero-day malware through machine learning: these techniques typically extract a series of features from the mobile device to send to a server where the detection model is located. Typically, these features include network traces or installed applications, among other information that may compromise user privacy. In this context, Federated learning is emerging with privacy advantages because the raw data never leaves the local device. In this paper, we propose a method to integrate federated machine learning in malware detection.Malicious software typically aims to extract sensitive and private data, and mobile devices emerge as particularly enticing targets from the perspective of attackers. In the experimental analysis, comprising a pool of 10 clients from which 7 are uniformly sampled at each round, we demonstrate the efficacy of the proposed method by achieving an accuracy of 0.940.

Paper Citation

in Harvard Style

Ciaramella G., Martinelli F., Mercaldo F., Peluso C. and Santone A. (2024). An Approach for Privacy-Preserving Mobile Malware Detection Through Federated Machine Learning. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 553-563. DOI: 10.5220/0012730600003690

in Bibtex Style

@conference{iceis24,
author={Giovanni Ciaramella and Fabio Martinelli and Francesco Mercaldo and Christian Peluso and Antonella Santone},
title={An Approach for Privacy-Preserving Mobile Malware Detection Through Federated Machine Learning},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={553-563},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012730600003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - An Approach for Privacy-Preserving Mobile Malware Detection Through Federated Machine Learning
SN - 978-989-758-692-7
AU - Ciaramella G.
AU - Martinelli F.
AU - Mercaldo F.
AU - Peluso C.
AU - Santone A.
PY - 2024
SP - 553
EP - 563
DO - 10.5220/0012730600003690
PB - SciTePress