SaTC: CORE: Medium: Collaborative: Understanding and Discovering Illicit Online Business Through Automatic Analysis of Online Text Traces

SaTC：核心：媒介：协作：通过自动分析在线文本痕迹理解和发现非法在线业务

• 批准号: 1801652
• 负责人: ChengXiang Zhai
• 金额: $ 30万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1801652&HistoricalAwards=false
• 关键词: SaTC; CORE; Medium; Collaborative; Understanding

Unlawful online business often leaves behind human-readable text traces for interacting with its targets (e.g., defrauding victims, advertising illicit products to intended customers) or coordinating among the criminals involved. Such text content is valuable for detecting various types of cybercrimes and understanding how they happen, the perpetrator's strategies, capabilities and infrastructures and even the ecosystem of the underground business. Automatic discovery and analysis of such text traces, however, are challenging, due to their deceptive content that can easily blend into legitimate communication, and the criminal's extensive use of secret languages to hide their communication, even on public platforms (such as social media and forums). The project aims at systematically studying how to automatically discover such text traces and intelligently utilize them to fight against online crime. The research outcomes will contribute to more effective and timely control of online criminal activities, and the team's collaboration with industry also enables the team to get feedback and facilitate the transformation of new techniques to practical use. This project focuses on both criminals' communication with their targets and the underground communications among miscreants. To discover and understand illicit online activities, the research looks for any semantic inconsistency between text content and its context (such as advertisements for selling illegal drugs on an .edu domain) and for inappropriate operations being triggered (such as a malware download). Inconsistencies are captured by the Natural Language Processing (NLP) techniques customized to various security settings. Further, based upon crime-related content discovered, the project will study various machine learning techniques that support automatic extraction and analysis of threat intelligence and criminal activities. The techniques are evaluated using data collected from various sources (public datasets, underground forums and others), and the findings they make are validated through a process that involves manual labeling, communication with affected parties, and collaborations with industry partners. This work will help create in-depth knowledge about underground ecosystems and lead to more effective control of illicit operations of these online businesses.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.

非法在线业务经常留下人类可读的文本痕迹，用于与其目标进行交互（例如，欺诈受害者、向目标客户宣传非法产品）或在所涉犯罪分子之间进行协调。这些文本内容对于检测各种类型的网络犯罪并了解其发生方式、犯罪者的策略、能力和基础设施，甚至地下业务的生态系统都很有价值。 然而，自动发现和分析这些文本痕迹是具有挑战性的，因为它们的欺骗性内容可以很容易地融入合法通信，并且犯罪分子广泛使用秘密语言来隐藏他们的通信，即使在公共平台上（如社交媒体和论坛）。该项目旨在系统地研究如何自动发现这些文本痕迹，并智能地利用它们来打击在线犯罪。研究成果将有助于更有效和及时地控制在线犯罪活动，该团队与行业的合作也使该团队能够获得反馈，并促进新技术的实际应用。 该项目的重点是罪犯与其目标的通信和歹徒之间的地下通信。为了发现和理解非法在线活动，该研究寻找文本内容及其上下文之间的任何语义不一致（例如在.edu域名上销售非法药物的广告）以及触发的不适当操作（例如恶意软件下载）。入侵是通过针对各种安全设置定制的自然语言处理（NLP）技术捕获的。此外，根据发现的犯罪相关内容，该项目将研究各种机器学习技术，以支持自动提取和分析威胁情报和犯罪活动。这些技术使用从各种来源（公共数据集，地下论坛等）收集的数据进行评估，并通过手动标记，与受影响方沟通以及与行业合作伙伴合作的过程验证他们的发现。这项工作将有助于深入了解地下生态系统，并导致更有效地控制这些在线业务的非法操作。该奖项反映了NSF的法定使命，并已被认为是值得支持的，通过评估使用基金会的知识价值和更广泛的影响审查标准。

项目成果

Leveraging Book Indexes for Automatic Extraction of Concepts in MOOCs

• DOI: 10.1145/3386527.3406749
• 发表时间: 2020-08
• 期刊: Proceedings of the Seventh ACM Conference on Learning @ Scale
• 作者: Assma Boughoula;Aidan San;Chengxiang Zhai

A Study of Methods for the Generation of Domain-Aware Word Embeddings

• DOI: 10.1145/3397271.3401287
• 发表时间: 2020-07
• 期刊: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
• 作者: Dominic Seyler;Chengxiang Zhai

Towards Dark Jargon Interpretation in Underground Forums

• DOI: 10.1007/978-3-030-72240-1_40
• 发表时间: 2020-11
• 期刊: ArXiv
• 作者: Dominic Seyler;Wei Liu;Xiaofeng Wang;Chengxiang Zhai

Retrieving Webpages Using Online Discussions

• DOI: 10.1145/3578337.3605139
• 发表时间: 2023-08
• 期刊: Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval
• 作者: Kevin Ros;Matthew Jin;Jacob Levine;ChengXiang Zhai

Fine Grained Categorization of Drug Usage Tweets

药物使用推文的细粒度分类

• DOI: 10.1007/978-3-031-05061-9_19
• 发表时间: 2022
• 期刊: User Experience and Impact (SCSM 2022
• 作者: Priyanka Dey;ChengXiang Zhai
• 通讯作者: ChengXiang Zhai