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一项关键挑战。有一些工具可以在在线浏览期间保护个人，但这些工具并不是针对发育障碍(例如自闭症)等弱势群体量身定做的。如果没有专门的支持来协助识别潜在的诈骗帖子中的威胁，这些人就会成为目标。该项目旨在了解自闭症和注意力缺陷/多动障碍(ADHD)患者表现出的独特理解和注意力模式，以改进诈骗检测工具，以帮助这些亚群。该项目的新颖性包括一种结合社会计算、认知心理学、特殊教育和计算语言学研究的多学科方法，以解决诈骗检测工具中使用的自然语言处理(NLP)人工智能方法中存在的偏见，该方法基于对易受攻击亚群显示的浏览模式的行为研究。该项目的更广泛意义在于将对人类行为的洞察融入网络安全工具，导致更好地保护脆弱群体，并在网络安全方面具有更大的包容性。这个项目追求两个目标。首先，它开发了一项眼球跟踪研究，以发现当接触到诈骗和合法的社交媒体帖子时，患有和没有发育障碍的人群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的诈骗检测模型的标签数据集中的表征偏差。它还创建了一组新颖的语言属性，可用于训练为帮助易受攻击的亚群而量身定做的诈骗检测模型。项目成果包括更好地了解针对弱势群体的社交媒体诈骗，开发用于诈骗检测的包容性NLP模型，以及通过定制诈骗警报帮助发育残疾个人的开放源码浏览器插件原型。该项目还创建了一个网站门户(Inclusive-Scambuster)，托管标记的骗局数据集，以突出代表性偏见和开源教育资源，以支持特殊教育项目在教学和培训网络犯罪预防方面的计划。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.