SaTC: CORE: Small: Enabling Systematic Evaluation of the Soundness of Android Security Analysis Techniques

SaTC：CORE：小型：支持对 Android 安全分析技术的健全性进行系统评估

• 批准号: 1815336
• 负责人: Adwait Nadkarni
• 金额: $ 50万
• 依托单位: College of William and Mary
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815336&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; Enabling; Systematic

Mobile devices have become the fabric of our current consumer computing landscape, driven by the diverse "apps" they support, which allow users to carry out complex computing tasks. These devices and apps have become deeply personal, and as such have access to privacy-sensitive resources and information. To prevent misuse of this access, it is imperative to understand the challenges in securing mobile apps, and in effect, the true capabilities of current approaches for security analysis. The first line of defense against malicious applications are tools that analyze applications to detect security vulnerabilities or malicious behavior, ideally before apps are published to application markets. It is important for such tools to be "sound", i.e., to detect all instances of bad behavior in the application being analyzed. Prior work has indicated that such tools may often sacrifice soundness for practical reasons (e.g., for a reasonable analysis time), leading to tools that are soundy (i.e., mostly sound, but with some unsound assumptions). However, the effectiveness of these security-focused program analysis techniques is not well understood, as the unsound assumptions may not be known beyond a small community of experts, leading to a false sense of security among the users of such tools. This research develops a framework for systematically evaluating existing security techniques to uncover previously unknown unsound assumptions. The methodology developed in the course of this research has the potential for a large economic and societal impact, as improving security tools improves the vetting of mobile applications and prevents or mitigates the loss or theft of private information. Moreover, while the far-reaching impact of this work will be in increasing reliability of applications for end users, more immediate impact can be seen in educational activities. That is, this project incorporates into software engineering and security courses the best-practices for secure mobile application development, and novel security evaluation techniques developed via this project, while disseminating awareness regarding the dangers of unsound analyses to the broader research community.This research project develops a new methodology for assessing security-focused static analysis tools for Android, by adopting the principles of mutation analysis from the field of functional software testing to systematically identify unsound assumptions. This methodology consists of three major components: (1) empirical derivation of specifications for security-focused mutation operators (i.e., security operators) through the examination of claims by security analysis tools, security bugs in open source apps, and malware samples; (2) the instantiation and seeding operators in the context of the security technique being evaluated using context-specific mutation schemes; and (3) expansion of the sound core of static program analysis tools for Android by running static analysis tools on mutants and detecting and addressing vulnerabilities. This research project cuts across the complementary disciplines of computer security and software engineering and tackles several open research questions that are currently unaddressed in both disciplines. More specifically, this project builds upon the theoretical underpinnings of mutation analysis, which aims at evaluating the efficacy of functional software tests and instantiates them in a security-focused context.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

移动设备已成为我们当前的消费者计算领域的结构，这些景观是由他们支持的各种“应用程序”驱动的，允许用户执行复杂的计算任务。 这些设备和应用程序已经变得非常个人化，因此可以访问对隐私敏感的资源和信息。 为了防止滥用此访问，必须了解确保移动应用程序的挑战，并实际上是当前安全分析方法的真正功能。针对恶意应用程序的第一道防线是分析应用程序以检测安全漏洞或恶意行为的工具，理想情况下，在将应用程序发布到应用程序市场之前。这样的工具必须是“声音”，即检测被分析应用程序中所有不良行为的实例。先前的工作表明，这种工具通常出于实际原因（例如，在合理的分析时间）可能牺牲合理性，从而导致工具很合理（即大部分是合理的，但有一些不愉快的假设）。但是，这些以安全为中心的程序分析技术的有效性尚不清楚，因为在小的专家社区之外，不知道的假设可能并不知道，从而导致此类工具的用户之间存在错误的安全感。这项研究开发了一个框架，用于系统地评估现有的安全技术，以揭示以前未知的不明化假设。在这项研究过程中开发的方法有可能产生巨大的经济和社会影响，因为改善安全工具可以改善移动应用程序的审查，并防止或减轻私人信息的损失或盗窃。此外，尽管这项工作的深远影响将是提高最终用户应用程序的可靠性，但在教育活动中可以看到更直接的影响。也就是说，该项目将通过该项目开发的安全移动应用程序开发的最佳实践纳入软件工程和安全课程中，同时传播了对对更广泛研究社区不健全分析的危险的认识，这项研究项目开发了一种新的方法，可通过评估系统的静态分析工具，通过对系统进行确定的静态分析工具，该方法通过采用和原理的定义，这些方法是传染性的，该工具是在采用和原理的，该研究的定义是，该项目的定义是，该方法是传统的，这些方法是传统的，这些方法是针对系统的，该研究项目是针对系统的，该研究项目是针对系统的，该研究项目是针对整体进行的。假设。该方法由三个主要组成部分组成：（1）通过安全分析工具，开源应用程序中的安全错误和恶意软件示例中的安全错误和示例的安全错误，对以安全为中心的突变操作员（即安全运营商）的规格进行了经验推导； （2）在使用上下文特异性突变方案评估的安全技术的背景下，实例化和播种操作员； （3）通过运行有关突变体以及检测和解决漏洞的静态分析工具来扩展Android的静态程序分析工具的声音核心。该研究项目介绍了计算机安全和软件工程的互补学科，并解决了几个开放研究问题，这些问题目前在这两个学科中都尚未得到解决。更具体地说，该项目基于突变分析的理论基础，该项目旨在评估功能软件测试的功效，并在以安全性为中心的上下文中实例化。该奖项反映了NSF的法定任务，并认为使用该基金会的知识分子功能和广泛的影响来评估CRETIRIA，以评估的支持值得通过评估。

项目成果

Discovering Flaws in Security-Focused Static Analysis Tools for Android using Systematic Mutation

• 发表时间: 2018-06
• 期刊: ArXiv
• 作者: R. Bonett;Kaushal Kafle;Kevin Moran;Adwait Nadkarni;D. Poshyvanyk

DeepMutation: A Neural Mutation Tool

DeepMutation：神经突变工具

• 发表时间: 2020
• 期刊: 2020 IEEE/ACM 42nd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion
• 作者: Tufano, Michele and

µSE: Mutation-Based Evaluation of Security-Focused Static Analysis Tools for Android

µSE：基于突变的 Android 安全静态分析工具评估

• DOI: 10.1109/icse-companion52605.2021.00034
• 发表时间: 2021
• 期刊: 2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion
• 作者: Ami, Amit Seal;Kafle, Kaushal;Nadkarni, Adwait;Poshyvanyk, Denys;Moran, Kevin
• 通讯作者: Moran, Kevin

Systematic Mutation-Based Evaluation of the Soundness of Security-Focused Android Static Analysis Techniques

• DOI: 10.1145/3439802
• 发表时间: 2021-02
• 期刊: ACM Transactions on Privacy and Security (TOPS)
• 作者: Amit Seal Ami;Kaushal Kafle;Kevin Moran;Adwait Nadkarni;D. Poshyvanyk

A Study of Data Store-based Home Automation

• DOI: 10.1145/3292006.3300031
• 发表时间: 2018-12
• 期刊: Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy
• 作者: Kaushal Kafle;Kevin Moran;Sunil Manandhar;Adwait Nadkarni;D. Poshyvanyk