SaTC: CORE: Small: GOALI: Predicting and Labeling Email Phishing from Social Influence Cues and User Characteristics.

SaTC：核心：小：GOALI：根据社会影响线索和用户特征预测和标记电子邮件网络钓鱼。

• 批准号: 2028734
• 负责人: Renato Figueiredo
• 金额: $ 50万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028734&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; GOALI; Predicting

Phishing is a dangerous and popular cyber attack that exerts high costs on Internet users and corporations and that has strong implications for national security. Most anti-phishing solutions have focused on automatic detection via a combination of vetting of known malicious websites (blocklists) and using machine learning to filter phishing. Although successful in practice, these approaches cannot prevent new phishing strategies from reaching users because determining malicious intent in text remains an unsolved problem. Messages and websites used in attacks constantly change, rendering blocklists and learning models quickly outdated. This project is developing a warning tool for web mail that exposes to users a key invariant of phishing attacks: the application of persuasive influence in the message text, framing the message as either potentially causing a gain or a loss, and creating emotional salience of its content (positive or negative). This project is performed in collaboration with the Google Security and Anti-Abuse Team. It has large potential for technology transfer and organic collaboration between industry and university. The project also has an educational component through a workshop on phishing awareness for a K-12 public school in Gainesville, Florida.The tool to be created and tested relies on a novel machine learning framework that exposes different types of influence cues in text via a combination of topic modeling, sentiment analysis, and standard machine learning algorithms. Warnings are generated according to an estimated user susceptibility score for the email, which is computed via susceptibility datasets in collaboration with Google. Tool evaluation is based on a behavioral-based user study that investigates whether or not the tool reduces real-life susceptibility to email phishing. The overall goals are to improve human-based detection and users' decision-making processes, and to reduce users' likelihood of falling for phishing.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.

网络钓鱼是一种危险而流行的网络攻击，对互联网用户和企业造成高昂的成本，并对国家安全产生重大影响。大多数反网络钓鱼解决方案都专注于通过审查已知恶意网站（阻止列表）和使用机器学习来过滤网络钓鱼的组合进行自动检测。虽然在实践中取得了成功，但这些方法无法阻止新的网络钓鱼策略到达用户，因为确定文本中的恶意意图仍然是一个未解决的问题。攻击中使用的消息和网站不断变化，使阻止列表和学习模型迅速过时。该项目正在开发一个用于网络邮件的警告工具，向用户展示网络钓鱼攻击的一个关键不变量：在消息文本中应用说服性影响，将消息框定为可能导致收益或损失，并创建其内容的情感突出性（积极或消极）。该项目是与谷歌安全和反滥用团队合作进行的。它具有巨大的技术转让潜力，并在工业和大学之间进行有机合作。该项目还通过为佛罗里达盖恩斯维尔的一所K-12公立学校举办关于网络钓鱼意识的讲习班而具有教育组成部分。要创建和测试的工具依赖于一种新颖的机器学习框架，该框架通过主题建模，情感分析和标准机器学习算法的组合来暴露文本中不同类型的影响线索。邮件是根据电子邮件的估计用户敏感性得分生成的，该得分是通过与Google合作的敏感性数据集计算的。 工具评估基于基于行为的用户研究，该研究调查该工具是否降低了对电子邮件网络钓鱼的真实敏感性。总体目标是改善基于人类的检测和用户的决策过程，并减少用户陷入网络钓鱼的可能性。该奖项反映了NSF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。

项目成果

People Still Care About Facts: Twitter Users Engage More with Factual Discourse than Misinformation

人们仍然关心事实：Twitter 用户更多地参与事实性话语而不是错误信息

• 发表时间: 2023
• 期刊: 9th International Symposium on Security and Privacy in Social Networks and Big Data (SocialSec
• 作者: Luiz Giovanini, Shlok Gilda

What Makes Disinformation Ads Engaging? A Case Study of Facebook Ads from the Russian Active Measures Campaign

• DOI: 10.1080/15252019.2023.2173991
• 发表时间: 2023-02
• 期刊: Journal of Interactive Advertising
• 作者: Mirela Silva;Luiz H. F. Giovanini;Juliana Fernandes;Daniela Oliveira;Catia S. Silva

Lumen: A machine learning framework to expose influence cues in texts

Lumen：揭示文本中影响线索的机器学习框架

• DOI: 10.3389/fcomp.2022.929515
• 发表时间: 2022
• 期刊: Frontiers in Computer Science
• 影响因子: 2.6
• 作者: Shi, Hanyu;Silva, Mirela;Giovanini, Luiz;Capecci, Daniel;Czech, Lauren;Fernandes, Juliana;Oliveira, Daniela
• 通讯作者: Oliveira, Daniela