Maloid-DS: Labeled Dataset for Android Malware Forensics

doi:10.1109/ACCESS.2024.3400211

2024-01-01

Iman Almomani *

Tala Almashat

Walid El‐Shafai *

DOI：10.1109/ACCESS.2024.3400211

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摘要

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Billions of people globally use Android devices (https://backlinko.com/iphone-vs-android-statistics). As such, these devices are highly targeted by security attackers. One of the most threatening attacks is to infect devices with malicious software (malware). Fortunately, there are various ways to counteract these attacks and prevent them. One of these methods is developing a comprehensive malware dataset that researchers can utilize for malware analysis, detection, prediction, and prevention systems. This paper introduces a unique, up-to-date, labeled Android malware dataset (Maloid-DS) comprising a comprehensive set of malware families that reached 345 families with 47,971 malware samples. First, we intensely studied existing datasets utilized by previous research works. These datasets are limited in (a) the number of studied families, (b) the number of samples under each family, (c) the number of new malware samples, (d) the proper categorization of the malware families, (e) the accurate mapping of the sample with its corresponding malware family, (f) providing well structuring of the malware families and subfamilies, and (g) presenting a profound description of each family behavior. All these limitations were seriously tackled by introducing Maloid-DS. The process of creating Maloid is detailed in this paper. Moreover, several case studies are demonstrated in this paper to show the value of Maloid and how different types of analysis systems and AI-based detection and prediction solutions could utilize it. While the full potential of Maloid-DS in real-world scenarios is subject to ongoing research and practical application, it represents a substantial contribution to the cybersecurity community, offering a broad and detailed foundation for protecting Android devices against malware threats.

Keyword:

Android OS

malware forensics

labeled datasets

deep learning

malware analysis

detection and classification

cybersecurity applications