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

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.

3rd International Conference on Information Systems Security and Privacy (ICISSP 2017), Porto, Portugal, 2017

External authors: Aniello Cimitile (Dipartimento di Ingegneria, Università degli Studi del Sannio)
IIT authors:

Type: Contributo in atti di convegno
Field of reference: Computer Science & Engineering

File: machine-learning-meets.pdf

Activity: Sicurezza di dispositivi mobili