ABSTRACT

As predictive analytics increasingly applies supervised machine learning (SML) models to inform mission-critical decision-making, adversaries become incentivized to exploit the vulnerabilities of these SML models and mislead predictive analytics into erroneous decisions. Due to the limited understanding and awareness of such adversarial attacks, the predictive analytics knowledge and deployment need a principled technique for adversarial robustness assessment and enhancement. In this research, we leverage the technology threat avoidance theory as the kernel theory and propose a research framework for assessing and enhancing the adversarial robustness of predictive analytics applications. We instantiate the proposed framework by developing a robust text classification system, the ARText system. The proposed system is rigorously evaluated in comparison with benchmark methods on two tasks extensively enabled by SML: spam review detection and spam email detection, which then confirmed the utility and effectiveness of our ARText system. Results from numerous experiments revealed that our proposed framework could significantly enhance the adversarial robustness of predictive analytics applications.

摘要

随着预测分析越来越多地应用监督机器学习 (SML) 模型来为关键任务决策提供信息，攻击者被激励利用这些 SML 模型的漏洞并将预测分析误导为错误决策。由于对此类对抗性攻击的理解和认识有限，预测分析知识和部署需要一种用于对抗性鲁棒性评估和增强的原则性技术。在这项研究中，我们利用技术威胁避免理论作为核心理论，并提出了一个研究框架来评估和增强预测分析应用程序的对抗鲁棒性。我们通过开发强大的文本分类系统 ARText 系统来实例化提出的框架。与 SML 广泛支持的两项任务的基准方法相比，对所提出的系统进行了严格的评估：垃圾邮件审查检测和垃圾邮件检测，然后证实了我们的 ARText 系统的实用性和有效性。大量实验的结果表明，我们提出的框架可以显着增强预测分析应用程序的对抗鲁棒性。