Collaborative Research: SaTC: CORE: Medium: Defending Against Social Engineering Attacks with In-Browser AI

基于web的社会工程攻击代表了一种日益增长的网络攻击，这种攻击通过借口、诱饵和网络钓鱼来利用人类决策过程中的弱点。这些攻击旨在欺骗用户执行可能具有关键网络安全和隐私影响的在线操作。例如，用户可能会被恶意网站欺骗，泄露敏感的个人信息或在他们的设备中安装恶意软件，因为他们认为他们可以免费获得某些东西（例如礼品卡）。该项目通过构建新颖而强大的实时浏览器内防御，在用户受到影响之前使用人工智能方法动态检测和阻止此类基于网络的社会工程攻击，从而使互联网更安全。通过与谷歌和AARP的协作，项目工件具有巨大的转化为实际使用的潜力。此外，该项目涉及三个机构的活动，以扩大代表性不足的群体对计算机的参与。现有的网络防御通常依赖于反应性方法（例如，黑名单），而不能解决社会工程攻击。与以前的方法不同，本研究引入了一个新的框架，用于发现、建模和防御桌面和移动环境下基于web的社会工程攻击。在发现方面，该项目引入了一个网络爬虫，用于自动收集、分析和分类社会工程攻击实例，同时考虑到不同的浏览设备。鉴于爬虫的发现，该项目使用机器学习方法来模拟攻击的浏览器内行为。最后，为了保护用户，该项目引入了实时浏览器内防御系统，该系统跟踪网页和网络推送通知是如何传递给用户的，监控它们在浏览器内的执行情况，并提取视觉特征以及网络和网络内容元数据。总的来说，该项目的成果提高了研究界对基于web的社会工程攻击的理解，并在保护用户免受这些攻击方面产生了实际影响。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。

Web-based social engineering attacks represent a growing class of cyber-attacks that exploit weaknesses in humans' decision-making processes via pretexts, baiting, and phishing. These attacks aim at deceiving users into performing online actions that may have critical cyber security and privacy implications. For instance, users may be deceived by malicious websites into revealing sensitive personal information or installing malicious software in their devices because they believe they would get something for free (e.g., a gift card). This project makes the Internet safer by building novel and robust real-time in-browser defenses that use artificial intelligence methods to dynamically detect and block such kinds of web-based social engineering attacks before users are affected. The project artifacts have immense potential to transition to practical use via collaboration with Google and AARP. Furthermore, the project involves activities across three institutions to broaden the participation of underrepresented groups in computing.Existing web defenses often rely on reactive approaches (e.g., blocklists) that do not address social engineering attacks. Unlike previous approaches, this research introduces a novel framework for discovering, modeling, and defending against web-based social engineering attacks on both desktop and mobile environments. On the discovery front, this project introduces a web-crawler to automatically harvest, analyze, and categorize instances of social-engineering attacks, considering different browsing devices. Given the discoveries of the crawler, this project uses machine-learning approaches to model the in-browser behavior of the attacks. Finally, to defend users, the project introduces real-time in-browser defense systems that track how web pages and web push notifications are delivered to users, monitor how they are executed within the browser, and extract visual features as well as network and web-content metadata. Overall, this project's outcomes improve the research community's understanding of web-based social-engineering attacks and exerts practical impact in protecting users against these attacks.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.