Effective classification of android malware families through dynamic features and neural networks

Due to their open nature and popularity, Android-based devices have attracted several end-users around the World and are one of the main targets for attackers. Because of the reasons given above, it is necessary to build tools that can reliably detect zero-day malware on these devices. At the moment, many of the frameworks that have been proposed to detect malware applications leverage Machine Learning (ML) techniques. However, an essential requirement to build these frameworks consists of using very large and sophisticated datasets for model construction and training purposes. Their success, indeed, strongly depends on the choice of the right features used for building a classification model providing adequate generalisation capability. Furthermore, the creation of a training dataset that well represents the malware properties and behaviour is one of the most critical challenges in malware analysis. Therefore, the main aim of this paper is proposing a new dataset called Unisa Malware Dataset (UMD) available on , which is based on the extraction of static and dynamic features characterising the malware activities. Additionally, we will show some experiments concerning common ML tools to demonstrate how it is possible to build efficient ML-based malware classification frameworks using the proposed dataset.

Automated summary

This paper proposes a new dataset called Unisa Malware Dataset (UMD) for static and dynamic feature extraction characterizing malware activities. Through experiments with common machine learning tools, it demonstrates how to build efficient ML-based malware classification frameworks using the proposed dataset.