Betrayed by Apps: Automated, Scalable Detection of Mobile App Malpractices

被应用程序背叛：移动应用程序故障的自动、可扩展检测

• 批准号: DP220102520
• 负责人: Prof Aruna Seneviratne
• 金额: $ 29.13万
• 依托单位: The University of New South Wales
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP220102520
• 关键词: Betrayed; Apps; Automated; Scalable; Detection

This project aims to develop a novel framework to detect content and privacy malpractices perpetrated by thousands of mobile apps. It will use innovative models and algorithms to achieve unprecedented levels of automation and scalability, making it possible for the first time to identify compliance violations across the global app ecosystem. Outcomes will include a knowledge base of prevalent app malpractices, detection algorithms, and a software framework for scalable app analysis. New evidence and tools will benefit both Australian and global policymakers and regulators in combating malpractices, users in identifying safe mobile apps for themselves, and local and global app market stakeholders in being more diligent about compliance.

该项目旨在开发一个新的框架来检测数千个移动应用程序的内容和隐私违规行为。它将使用创新的模型和算法来实现前所未有的自动化和可扩展性，从而首次有可能在全球应用生态系统中识别违规行为。结果将包括流行的应用程序违规行为的知识库、检测算法和用于可扩展应用程序分析的软件框架。新的证据和工具将有利于澳大利亚和全球的政策制定者和监管机构打击不当行为，用户为自己识别安全的移动应用程序，以及当地和全球应用程序市场的利益相关者更加勤奋地遵守法规。