Mobile Malware Classification for iOS Inspired by Phylogenetics

Abstract

Cyber-attacks such as ransomware, data breaches, and phishing triggered by malware, especially for iOS (iPhone operating system) platforms, are increasing. Yet not much works on malware detection for the iOS platform have been done compared to the Android platform. Hence, this paper presents an iOS malware classification inspired by phylogenetics. It consists of mobile behaviour, exploits, and surveillance features. The new iOS classification helps to identify, detect, and predict any new malware variants. The experiment was conducted by using hybrid analysis, with twelve (12) malwares datasets from the Contagio Mobile website. As a result, twenty-nine (29) new classifications have been developed. One hundred (100) anonymous mobile applications (50 from the Apple Store and 50 from iOS Ninja) have been used for evaluation. Based on the evaluation conducted, 13% of the mobile applications matched with the developed classifications. In the future, this work can be used as guidance for other researchers with the same interest.

iOSDBuilder: Uma Ferramenta de Construção de Datasets para Detecção de Malwares iOS

“…O problema não está na falta de modelos de aprendizado de máquina. Trabalhos como [Cimitile et al 2017, Husainiamer et al 2021, Garg and Baliyan 2021, Yixiang and Kang 2017 mostram o emprego de diferentes métodos e classificadores para identificar malwares em aplicativos iOS. O problema está na falta de conjuntos de dados (datasets) para estas análises, isto é, há uma escassez de insumos relacionados à aplicativos iOS.…”