EAGER: DCL: SaTC: EIC: Inclusive-ScamBuster: Inclusive Scam Detection Methods for Social Media to Design Assistive Tools for Protecting Individuals with Developmental Disabilities

防止基于社交媒体的诈骗是网络安全的关键挑战。有一些工具可以在网上浏览时保护个人，然而，这些工具并不是针对像发育障碍（如自闭症）这样的弱势群体量身定制的。这些人在没有专门支持的情况下成为目标，以协助识别潜在的骗局帖子中的威胁。本项目旨在了解自闭症和注意力缺陷/多动障碍（ADHD）患者所表现出的独特的理解和注意力模式，以改进诈骗检测工具来帮助这些亚群。该项目的新颖之处包括结合社会计算、认知心理学、特殊教育和计算语言学研究的多学科方法，以解决欺诈检测工具中使用的自然语言处理（NLP）人工智能方法中的现有偏见，该方法基于对弱势群体显示的浏览模式的行为研究。该项目的更广泛意义在于将人类行为的见解整合到网络安全工具中，从而更好地保护弱势群体，提高网络安全的包容性。这个项目有两个目标。首先，它开展了一项眼球追踪研究，以发现在接触骗局和合法的社交媒体帖子时，在有和没有发育障碍的人群中观察到的注意力模式的变化。其次，它使用观察到的注意模式的变化来突出基于nlp的骗局检测模型的标记数据集中的表示偏差。它进一步创建了一组新的语言属性，可用于训练专门为帮助弱势群体定制的骗局检测模型。项目成果包括更好地了解针对弱势群体的社交媒体骗局，开发用于骗局检测的包容性NLP模型，以及一个开源浏览器插件原型，通过量身定制的骗局警报来帮助有发育障碍的个人。该项目还创建了一个门户网站（Inclusive-ScamBuster），托管标记的诈骗数据集，以突出代表性偏见和开源教育资源，以支持特殊教育项目的教学和培训网络犯罪预防。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Preventing social media-based scams is a critical challenge for cybersecurity. There exist tools to protect individuals during online browsing, however, they are not tailored towards vulnerable subpopulations like individuals with developmental disabilities (e.g., Autism). Such individuals become targets without dedicated support to assist with threat identification in potential scam posts. This project aims to understand the distinctive comprehension and attention patterns displayed by individuals with Autism and Attention-Deficit/Hyperactivity Disorder (ADHD), to improve scam detection tools to assist these subpopulations. The project’s novelties include a multidisciplinary approach combining social computing, cognitive psychology, special education, and computational linguistics research to address existing biases in Artificial Intelligence methods of Natural Language Processing (NLP) used in scam detection tools, based on behavioral studies of browsing patterns displayed by vulnerable subpopulations. The project’s broader significance is in integrating insights of human behavior into cybersecurity tools, leading to better protection of vulnerable subpopulations and greater inclusiveness in cybersecurity. This project pursues two goals. First, it develops an eye-tracking study to discover variations in attention patterns observable across populations with and without developmental disabilities when exposed to scams and legitimate social media posts. Second, it uses observed variations in attention patterns to highlight representation biases in the labeled datasets of NLP-based scam detection models. It further creates a novel set of linguistic attributes that can be used to train scam detection models tailored to aid vulnerable subpopulations. Project outcomes include a better understanding of social media scams for vulnerable subpopulations, the development of an inclusive NLP model for scam detection, and an open-source browser plugin prototype to aid individuals with developmental disabilities via tailored scam alerts. The project also creates a web portal (Inclusive-ScamBuster) hosting labeled scam datasets to highlight representational biases and open-source educational resources to support Special Education programs in teaching and training cybercrime prevention.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.