Detecting Mobile Malware Associated With Global Pandemics
Detecting Mobile Malware Associated With Global Pandemics
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DOI：
10.1109/mprv.2023.3321218
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发表时间：
2023-10
影响因子：
1.6
通讯作者：
Alfredo J. Perez;S. Zeadally;David Kingsley Tan
中科院分区：
文献类型：
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作者：
Alfredo J. Perez;S. Zeadally;David Kingsley Tan
全球有超过60亿部智能手机可供使用，政府和公共卫生组织可以开发应用程序来管理全球流行病。然而，黑客可以利用这个机会，通过伪装成流行病相关应用程序的恶意软件，以邪恶的方式瞄准公众。最近在COVID-19大流行期间进行的一项分析显示，公众从不受信任的来源安装了几种与COVID-19相关的恶意软件变体。我们建议使用应用程序权限和一个额外的功能（权限总数）来开发一个使用机器学习（ML）模型的静态检测器，以便在安装时快速检测与流行病相关的Android恶意软件。使用2000多个COVID-19相关应用程序的数据集，并通过评估使用决策树和朴素贝叶斯创建的ML模型，我们的结果表明，使用具有应用程序权限和拟议功能的决策树模型，可以检测到流行病相关恶意软件应用程序，准确率超过90%。
More than 6 billion smartphones available worldwide can enable governments and public health organizations to develop apps to manage global pandemics. However, hackers can take advantage of this opportunity to target the public in nefarious ways through malware disguised as pandemics-related apps. A recent analysis conducted during the COVID-19 pandemic showed that several variants of COVID-19 related malware were installed by the public from nontrusted sources. We propose the use of app permissions and an extra feature (the total number of permissions) to develop a static detector using machine learning (ML) models to enable the fast-detection of pandemics-related Android malware at installation time. Using a dataset of more than 2000 COVID-19 related apps and by evaluating ML models created using decision trees and Naive Bayes, our results show that pandemics-related malware apps can be detected with an accuracy above 90% using decision tree models with app permissions and the proposed feature.