Comparing Classifiers: A Look at Machine-Learning and the Detection of Mobile Malware in COVID-19 Android Mobile Applications
Comparing Classifiers: A Look at Machine-Learning and the Detection of Mobile Malware in COVID-19 Android Mobile Applications
复制标题
比较分类器：机器学习和 COVID-19 Android 移动应用程序中移动恶意软件的检测
DOI：
10.1145/3565287.3617629
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Perez, Alfredo J.
中科院分区：
文献类型：
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作者：
Johnson, Seth;Donner, Ray;Perez, Alfredo J.
新冠肺炎大流行是我们世界各地日常生活中许多不同趋势的催化剂。虽然在此期间网络犯罪总体上有所上升，但对以新冠肺炎为主题的恶意安卓操作系统应用程序的研究相对较少。随着越来越多的用户成为新冠肺炎主题的恶意安卓应用程序的受害者，有必要了解针对流行病主题的移动应用程序的恶意软件检测。在这个项目中，我们从1959个APK文件中提取了权限请求，这些文件来自一个包含良性和恶意新冠肺炎主题应用程序的数据集。然后，我们创建并比较了四个不同分类器的八个唯一模型，以确定它们基于APK文件所请求的权限来识别潜在恶意APK文件的能力：支持向量机、神经网络、决策树和分类朴素贝叶斯。然后使用合成少数过采样技术(SMOTE)对这些分类器进行训练，以平衡数据集，因为与非恶意软件APK相比，恶意软件缺乏样本。最后，我们使用K-折叠交叉验证对模型进行评估，发现决策树分类器是性能最好的分类器。
The COVID-19 pandemic was a catalyst for many different trends in our daily life worldwide. While there has been an overall rise in cybercrime during this time, there has been relatively little research done about malicious COVID-19 themed AndroidOS applications. With the rise in reports of users falling victim to malicious COVID-19 themed AndroidOS applications, there is a need to learn about the detection of malware for pandemics-themed mobile apps.. In this project, we extracted the permissions requests from 1959 APK files from a dataset containing benign and malware COVID-19 themed apps. We then created and compared eight unique models of four varying classifiers to determine their ability to identify potentially malicious APK files based on the permissions the APK file requests: support vector machine, neural network, decision trees, and categorical naive bayes. These classifiers were then trained using Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset due to the lack of samples of malware compared to non-malware APKs. Finally, we evaluated the models using K-Fold Cross-Validation and found the decision tree classifier to be the best performing classifier.
COVID -19 大流行期间的网络犯罪
DOI：
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发表时间：
2021
期刊：
International Journal of Information Engineering and Electronic Business
影响因子：
--
作者：
Raghad Khweiled;M. Jazzar;Derar Eleyan
通讯作者：
Derar Eleyan