Machine Learning Meets iOS Malware: Identifying Malicious Applications on Apple Environment

Aniello Cimitile, Fabio Martinelli, Francesco Mercaldo

2017

Abstract

The huge diffusion of the so-called smartphone devices is boosting the malware writer community to write more and more aggressive software targeting the mobile platforms. While scientific community has largely studied malware on Android platform, few attention is paid to iOS applications, probably to their closed-source nature. In this paper, in order to fill this gap, we propose a method to identify malicious application on Apple environment. Our method relies on a feature vector extracted by static analysis. Experiments, performed with 20 different machine learning algorithms, demonstrate that malware iOS applications are discriminated by trusted ones with a precision equal to 0.971 and a recall equal to 1.

Paper Citation

in Harvard Style

Cimitile A., Martinelli F. and Mercaldo F. (2017). Machine Learning Meets iOS Malware: Identifying Malicious Applications on Apple Environment . In Proceedings of the 3rd International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-209-7, pages 487-492. DOI: 10.5220/0006217304870492

in Bibtex Style

@conference{icissp17,
author={Aniello Cimitile and Fabio Martinelli and Francesco Mercaldo},
title={Machine Learning Meets iOS Malware: Identifying Malicious Applications on Apple Environment},
booktitle={Proceedings of the 3rd International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2017},
pages={487-492},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006217304870492},
isbn={978-989-758-209-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Machine Learning Meets iOS Malware: Identifying Malicious Applications on Apple Environment
SN - 978-989-758-209-7
AU - Cimitile A.
AU - Martinelli F.
AU - Mercaldo F.
PY - 2017
SP - 487
EP - 492
DO - 10.5220/0006217304870492