高质量的数据集对于构建现实和高性能的受监督恶意软件检测模型至关重要。目前,基于机器学习的解决方案的主要挑战之一是既具有代表性又具有高质量的数据集的稀缺性。促进未来的研究,并为现有分类器的综合评估和比较提供更新和公开的数据,我们引入MH-100K数据集[1],广泛的Android恶意软件信息集合,包括101,975个样本。它包含一个包含有价值的元数据的主要CSV文件,包括SHA256哈希(APK\的签名),文件名,软件包名称,Android的官方编译API,166个权限,24,417个API调用,250种意图此外,MH-100K数据集具有广泛的文件集合,其中包含VirusTotal1分析的有用元数据。该信息库可以通过分析防病毒扫描结果模式来识别各种恶意软件家族的流行和行为,从而为未来的研究服务。这样的分析可以帮助扩展现有的恶意软件分类,新变体的鉴定,以及恶意软件随时间演变的探索。
High-quality datasets are crucial for building realistic and high-performance supervised malware detection models. Currently, one of the major challenges of machine learning-based solutions is the scarcity of datasets that are both representative and of high quality. To foster future research and provide updated and public data for comprehensive evaluation and comparison of existing classifiers, we introduce the MH-100K dataset [1], an extensive collection of Android malware information comprising 101,975 samples. It encompasses a main CSV file with valuable metadata, including the SHA256 hash (APK\'s signature), file name, package name, Android\'s official compilation API, 166 permissions, 24,417 API calls, and 250 intents. Moreover, the MH-100K dataset features an extensive collection of files containing useful metadata of the VirusTotal1 analysis. This repository of information can serve future research by enabling the analysis of antivirus scan result patterns to discern the prevalence and behaviour of various malware families. Such analysis can help to extend existing malware taxonomies, the identification of novel variants, and the exploration of malware evolution over time.