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

EAGER：DCL：SaTC：EIC：Inclusive-ScamBuster：社交媒体的包容性诈骗检测方法，用于设计保护发育障碍人士的辅助工具

• 批准号: 2210107
• 负责人: Hemant Purohit
• 金额: $ 29.92万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210107&HistoricalAwards=false
• 关键词: EAGER; DCL; SaTC; EIC; Inclusive

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.

防止基于社交媒体的诈骗是网络安全面临的一项重大挑战，目前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人士（例如自闭症患者）等弱势群体而设计的。协助识别潜在诈骗帖子中的威胁。该项目旨在了解自闭症和注意力缺陷/多动症 (ADHD) 患者所表现出的独特理解和注意力模式，以改进诈骗检测工具来帮助这些人。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，以基于浏览行为研究来解决诈骗检测工具中使用的自然语言处理（NLP）人工智能方法中的现有偏见。该项目更广泛的意义在于将人类行为的洞察融入到网络安全工具中，从而更好地保护弱势群体并提高网络安全的包容性。一项眼球追踪研究，旨在发现有或没有发育障碍的人群在接触诈骗和合法社交媒体帖子时可观察到的注意力模式的变化。 其次，它利用观察到的注意力模式的变化来突出基于 NLP 的标记数据集中的代表性偏差。它进一步创建了一组新颖的语言属性，可用于训练针对弱势群体的诈骗检测模型。项目成果包括更好地了解针对弱势群体的社交媒体诈骗、开发包容性 NLP 模型。诈骗检测，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价值和更广泛的影响审查标准进行评估，被认为值得支持。