Collaborative Research: EAGER: SaTC-EDU: Just-in-Time Artificial Intelligence-Driven Cyber Abuse Education in Social Networks

社交网络鼓励随意互动，并使用户面临各种形式的网络虐待，众所周知，这会产生负面的社会心理影响。之前的研究表明，只有一小部分网络虐待受害者采取了自我保护行为。这可能是因为一些受害者缺乏识别网络滥用和主张适当保护行为所需的背景知识。虽然教育在这方面可以有效，但课堂教学可能无法再现网络滥用的多样和动态背景，使学生难以有效地将知识转化为实践。该项目寻求通过将教育内容整合到社交网络互动中来增加自我保护行为的采用。人工智能(AI)技术将用于优化教育内容的放置和时机。该项目有可能改善易受攻击的社交网络用户的安全和隐私。该项目团队将利用他们在网络安全、人工智能和教育方面的专业知识，调查、开发和评估一个新的教育框架，该框架提供即时意识培训，以识别和适当地应对使用社交网络时的网络滥用行为。首先，该团队将开发基于人工智能的解决方案，根据相关用户账户中的滥用痕迹来检测和分类网络滥用。该团队还将利用从研究参与者那里收集的数据和反馈，建立网络滥用实例和时间表的实地数据集。其次，该团队将设计和实施有针对性的学习内容和用户界面提示，以提供在社交网络互动中做出更安全决策所需的知识。第三，该团队将开发基于人工智能的技术，以确定学习内容的理想位置，以提高用户对自我保护行为的接受。最后，将利用Facebook的独特功能来设计评估实验和基于教育结果的技术，以捕捉用户在Facebook常规互动环境中的行为。该项目由安全和值得信赖的网络空间(SATC)计划的一个特别倡议支持，该计划旨在促进网络安全、人工智能和教育领域之间的新的、以前未探索的合作。SATC计划与联邦网络安全研究和发展战略计划和国家隐私研究战略保持一致，以保护和维护网络系统日益增长的社会和经济效益，同时确保安全和隐私。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。

Social networks encourage casual interactions and expose users to a variety of forms of cyber abuse, which are known to have negative socio-psychological effects. Previous work has shown that only a fraction of cyber abuse victims adopt self-protective behaviors. This may occur because some victims lack the background knowledge required to identify cyber abuse and to assert appropriate protective behaviors. While education can be effective in this regard, classroom delivery may fail to reproduce the diverse and dynamic context of cyber abuse, making it difficult for students to effectively translate knowledge into practice. This project seeks to increase the adoption of self-protective behaviors by integrating educational content into social networking interactions. Artificial intelligence (AI) techniques will be used to optimize the placement and timing of educational content. The project has the potential to improve the security and privacy of vulnerable social network users.The project team will leverage their expertise in cybersecurity, AI, and education to investigate, develop and evaluate a new educational framework that provides just-in-time awareness training to identify and respond appropriately to cyber abuse when using social networks. First, the team will develop AI-based solutions to detect and classify cyber abuse based on abuse traces in the accounts of the users involved. The team will also leverage data and feedback collected from study participants to build a ground-truth dataset of instances and timelines of cyber abuse. Second, the team will design and implement targeted learning content and user interface nudges to deliver the knowledge required to make safer decisions in social network interactions. Third, the team will develop AI-based techniques to determine the ideal placement of learning content that improves user adoption of self-protective behaviors. Finally, the unique features of Facebook will be exploited to design evaluation experiments and educational outcomes-based techniques that capture user behaviors in the context of their regular Facebook interactions.This project is supported by a special initiative of the Secure and Trustworthy Cyberspace (SaTC) program to foster new, previously unexplored, collaborations between the fields of cybersecurity, artificial intelligence, and education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy.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.