CRII: SaTC: Empowering Elastic-honeypot as Real-time Malicious Content Sniffers for Social Networks

CRII：SaTC：使弹性蜜罐成为社交网络的实时恶意内容嗅探器

• 批准号: 1948374
• 负责人: Xu Yuan
• 金额: $ 17.5万
• 依托单位: University of Louisiana at Lafayette
• 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: 2020-03-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1948374&HistoricalAwards=false
• 关键词: CRII; SaTC; Empowering; Elastic; honeypot

Spam messages, misinformation, disinformation and outright fraud are rampant on social networks. To separate out such malicious content from benign and useful content and protect social networks, there is a need for robust content classification systems. However, before such systems can be designed, there is a need for data that train the classifiers. Honeypots are a good way to obtain such data about malicious attacker behavior. Conventional honeypots rely on manually created artificial user accounts as lures to trap attack activities. However, such honeypots are often identified easily by smart attackers. They also suffer from lack of deployment flexibility, feature variability, network scalability, and system portability. This project develops a novel and lightweight honeypot-based malicious content capturing system that cannot be easily bypassed by attackers. The honeypot is then used to intelligently gather and automatically classify contents into likely malicious and likely benign. The goal is to mitigate the adverse effects of malicious contents and sanitize social environments, significantly elevating the security and trust of social networks. Research datasets and software toolkits are shared with the broader research community. The research findings are transitioned into educational materials in the form of book chapters and hands-on classroom materials, delivered to students at the University of Louisiana at Lafayette and also shared with other universities worldwide. The project also involves undergraduate and under-represented students for research experience. This project develops a novel and lightweight honeypot-based malicious content sniffing system, named the elastic-honeypot sniffer, to overcome drawbacks in conventional honeypot-based solutions. Two core components constitute the elastic-honeypot sniffer: (1) real-time data gathering and (2) elastic-honeypot detector. Using robust learning techniques on existing spam datasets, the project identifies features and behavior profiles of users who are found to be lucrative targets for spammers. For real-time data gathering, the elastic-honeypot dynamically deploys artificial user accounts as lures based on the learnt vulnerable user profiles to trap attackers intelligently. The main advantages over conventional honeypot technology are node availability, deployment flexibility, features variability, network scalability, and system portability. The data captured by the elastic-honeypot sniffer is used to design robust classification techniques to differentiate between malicious and benign content that are resilient against adversarial 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.

垃圾短信、错误信息、虚假信息和彻头彻尾的欺诈在社交网络上猖獗。为了将此类恶意内容从良性和有用的内容中分离出来，并保护社交网络，需要强大的内容分类系统。然而，在设计这样的系统之前，需要训练分类器的数据。蜜罐是获取有关恶意攻击者行为的此类数据的好方法。传统的蜜罐依赖于手动创建的人工用户帐户作为诱饵来诱捕攻击活动。然而，这类蜜罐往往很容易被聪明的攻击者识别出来。它们还缺乏部署灵活性、功能可变性、网络可扩展性和系统可移植性。该项目开发了一种新颖且轻量级的基于蜜罐的恶意内容捕获系统，攻击者无法轻松绕过该系统。然后，蜜罐被用来智能地收集内容，并自动将内容分类为可能的恶意内容和可能的良性内容。其目标是减轻恶意内容的不利影响，净化社交环境，显著提升社交网络的安全性和信任度。研究数据集和软件工具包与更广泛的研究社区共享。研究成果以书籍章节和实践课堂材料的形式转化为教育材料，交付给路易斯安那大学拉斐特分校的学生，也与世界各地的其他大学分享。该项目还邀请了本科生和代表性不足的学生参加，以获得研究经验。该项目开发了一种新颖、轻量级的基于蜜罐的恶意内容嗅探系统，称为弹性蜜罐嗅探器，以克服传统基于蜜罐的解决方案的缺陷。弹性蜜罐嗅探器由两个核心部件组成：(1)实时数据采集和(2)弹性蜜罐检测器。该项目使用现有垃圾邮件数据集上的强大学习技术，识别被发现是垃圾邮件发送者有利可图的目标的用户的特征和行为特征。对于实时数据收集，弹性蜜罐根据学习到的易受攻击的用户配置文件动态部署人工用户帐户作为诱饵，以智能地诱捕攻击者。与传统的蜜罐技术相比，蜜罐技术的主要优势是节点可用性、部署灵活性、功能可变性、网络可扩展性和系统可移植性。弹性蜜罐嗅探器捕获的数据用于设计稳健的分类技术，以区分对对手攻击具有弹性的恶意和良性内容。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。

项目成果

Messy States of Wiring: Vulnerabilities in Emerging Personal Payment Systems

• 发表时间: 2021
• 作者: Jiadong Lou;Xu Yuan;Ning Zhang

Interpretable Minority Synthesis for Imbalanced Classification

• DOI: 10.24963/ijcai.2021/350
• 发表时间: 2021-08
• 作者: Yi He;Fudong Lin;Xu Yuan;N. Tzeng

Cascade Variational Auto-Encoder for Hierarchical Disentanglement

• DOI: 10.1145/3511808.3557254
• 发表时间: 2022-10
• 期刊: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
• 作者: Fudong Lin;Xu Yuan;Lu Peng;N. Tzeng

Active Learning with Multi-Granular Graph Auto-Encoder

• DOI: 10.1109/icdm50108.2020.00125
• 发表时间: 2020-11
• 期刊: 2020 IEEE International Conference on Data Mining (ICDM)
• 作者: Yi He;Xu Yuan;N. Tzeng;Xindong Wu

Unsupervised Lifelong Learning with Curricula

• DOI: 10.1145/3442381.3449839
• 发表时间: 2021-04
• 期刊: Proceedings of the Web Conference 2021
• 作者: Yi He;Sheng Chen;Baijun Wu;Xu Yuan;Xindong Wu