计算机应用与软件2024，Vol.41Issue(6)：312-319,8.DOI:10.3969/j.issn.1000-386x.2024.06.045
基于动态特征选择的Android应用隐私风险自动化检测
AUTOMATED DETECTION OF PRIVACY RISKS IN ANDROID APPLICATIONS BASED ON DYNAMIC FEATURE SELECTION
摘要
Abstract
Aimed at the user privacy leakage problem that may exist in Android applications,an automated detection model based on machine learning methods is proposed.This model chose to use the permission items applied by the App as features,dynamically selected the feature set,and used four classical machine learning algorithms to independently train and predict.And the most suitable privacy risk detection model for Android applications was determined.Experimental results show that the model can achieve an average prediction accuracy of more than 95％for privacy risk applications.This model can better manage application risk and protect user privacy from multiple aspects,which has high social benefit and practical value.关键词
隐私风险/权限/机器学习/动态特征选择Key words
Privacy risk/Permission/Machine learning/Dynamic feature selection分类
信息技术与安全科学引用本文复制引用
高龙良,杜素果,杨金萍..基于动态特征选择的Android应用隐私风险自动化检测[J].计算机应用与软件,2024,41(6):312-319,8.基金项目
国家自然科学基金项目(71671114). （71671114）
