CybercrimeNLP (CC-NLP): A natural language processing toolkit for the interdisciplinary analysis of underground online forums

Cyber​​crimeNLP (CC-NLP)：用于地下在线论坛跨学科分析的自然语言处理工具包

• 批准号: ES/T008466/1
• 负责人: Alice Hutchings
• 金额: $ 30.91万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FT008466%2F1
• 关键词: CybercrimeNLP; CC; NLP; natural; language

Online and electronic crime now account for about half of all property crime, in all countries for which we have good victimisation data. A significant number of other offences, including harassment, also happen online. It is therefore essential for criminologists, lawyers, social scientists, psychologists and others to be able to study online crime and work out what's going on.We are starting to have some really good sources of data, including more than 70 million messages scraped from underground crime forums in the CrimeBB database. There forums are where cyber-crooks meet up, trade tools and techniques, and sell each other services. They are a gold mine for criminologists studying how young people get drawn into crime; social scientists studying the evolution of political ideology, racism and homophobia; lawyers interested in criminal business models and how they respond to police interventions; and many others.The missing link at present is this: that scholars in the humanities and social sciences do not at present have the tools to deal with such large bodies of text. In the pre-Internet era, researchers might have interviewed a few dozen criminals, coded up the interviews by hand and analysed them using a statistics package; but dealing with millions of messages requires new approaches.This project will draw upon the discipline of natural-language processing to build tools that will enable scholars in the humanities and social sciences deal with these large volumes of text using modern techniques of artificial intelligence and machine learning (AI/ML). They will help researchers find topics of interest, identify the types of crime being discussed, search for messages that are similar in various ways to those already identified, track trends, and match users across forums. Users will be able to look for indicators that identify users who are just starting out (and might therefore be targeted with primary prevention approaches) as well as those who are becoming influential (and might therefore be worth more aggressive interventions). Our tools will also enable researchers to measure the effect of both crime-prevention initiatives and policing action, so that policymakers can gather evidence of what works and what doesn't.The tools we build will start to do for research with large text corpora drawn from crime forums, what search engines have done for the Internet -- namely making such resources accessible to researchers who do not have either technical skills or technical assistance. They will therefore enable much more use to be made of existing data resources, starting with the CrimeBB database (which was funded in a previous project funded by ESRC and EPSRC), but not limited to it. Their use by researchers in diverse disciplines will also enable us to learn about how NLP tools, and more generally AI/ML tools, can be used robustly. This is of independent importance given the current rush to use AI/ML techniques and the concern that some of these techniques may simply reflect the bias in their training data, leading naive researchers to just measure their own ruler. It's not enough just to invent new tools; we also have to figure out how to use them properly, and for that, it's vital to work with a community of scholars from multiple disciplines in the humanities and social sciences on a shared problem, using shared data, and where we have some access eventually to ground truth.

在线和电子犯罪现在约占所有财产犯罪的一半，在我们拥有良好的犯罪数据的所有国家中。包括骚扰在内的大量其他罪行也发生在网上。因此，对于犯罪学家、律师、社会科学家、心理学家和其他人来说，能够研究网络犯罪并弄清楚发生了什么是至关重要的。我们开始有一些非常好的数据来源，包括CrimeBB数据库中从地下犯罪论坛收集的7000多万条信息。这些论坛是网络骗子聚会的地方，交易工具和技术，并相互出售服务。对于研究年轻人如何被卷入犯罪的犯罪学家、研究政治意识形态、种族主义和同性恋恐惧症演变的社会科学家、对犯罪商业模式以及他们如何应对警方干预感兴趣的律师以及其他许多人来说，它们是一座金矿。目前缺失的一环是：人文和社会科学领域的学者目前没有处理如此大量文本的工具。在前互联网时代，研究人员可能会采访几十名罪犯，手工编码采访，并使用统计软件包进行分析;但处理数以百万计的信息需要新的方法。这个项目将借鉴自然学科-语言处理，以建立工具，使学者在人文和社会科学处理这些大量的文本使用现代技术的人工智能和机器学习（AI/ML）。它们将帮助研究人员找到感兴趣的主题，识别正在讨论的犯罪类型，搜索与已经确定的信息相似的各种信息，跟踪趋势，并在论坛上匹配用户。使用者将能够寻找指标，以确定哪些使用者刚刚起步（因此可能成为初级预防办法的目标），哪些使用者正在变得有影响力（因此可能值得采取更积极的干预措施）。我们的工具还将使研究人员能够衡量预防犯罪举措和警务行动的效果，以便政策制定者能够收集证据，证明哪些有效，哪些无效。我们构建的工具将开始用于研究来自犯罪论坛的大型文本语料库，搜索引擎为互联网做了什么--也就是说，使那些既没有技术技能也没有技术援助的研究人员能够获得这些资源。因此，它们将使我们能够更多地利用现有的数据资源，从CrimeBB数据库开始（该数据库是由ESRC和EPSRC资助的先前项目资助的），但不限于此。不同学科的研究人员使用它们也将使我们能够了解如何稳健地使用NLP工具，以及更普遍的AI/ML工具。考虑到目前急于使用AI/ML技术，以及担心其中一些技术可能只是反映了其训练数据中的偏差，导致天真的研究人员只是测量自己的标尺，这一点具有独立的重要性。仅仅发明新工具是不够的;我们还必须弄清楚如何正确使用它们，为此，与来自人文和社会科学多学科的学者社区合作，使用共享数据，解决共同的问题，以及我们最终获得地面真相的地方，这一点至关重要。

项目成果

Detecting Trending Terms in Cybersecurity Forum Discussions

• DOI: 10.18653/v1/2020.wnut-1.15
• 发表时间: 2020-11
• 作者: Jack Hughes;S. Aycock;Andrew Caines;P. Buttery;Alice Hutchings

Follow the money: The relationship between currency exchange and illicit behaviour in an underground forum

追随金钱：货币兑换与地下论坛非法行为之间的关系

• DOI: 10.1109/eurospw54576.2021.00027
• 发表时间: 2021
• 作者: Siu G