DroidDefender: An Image-based Android Antimalware Proof-of-Concept
Giovanni Ciaramella, Fabio Martinelli, Francesco Mercaldo, Antonella Santone · 2024
Malware analysis researchers are currently focused on the design and development of innovative approaches to detect zero-day malware, with particular regard to mobile environments. It is widely recognized that existing free and commercial anti-malware tools struggle to detect unknown threats, operating within the constraints of the signature-based paradigm. Among the set of techniques frequently exploited by researchers for spotting malware, machine learning, with particular regard to deep learning, is quickly gaining prominence as a highly promising method for zero-day malware detection. Unfortunately, many of the proposed methods are not effectively implemented in research prototypes and, therefore, do not truly become usable by end users. For this reason, in this paper, we present the DroidDefender proof-of-concept, an Android antimalware specifically designed and developed to detect zero-day malware. DroidDefender is based on representing an Android application as an image and employs a deep learning model. The DroidDefender research prototype is freely available for research purposes at the following URL: https://www.cybersecurityosservatorio.it/Services/malwareImage.jsp?lang=en.