Behavioural characteristics of cyber-criminals in online trading

网络犯罪分子在网络交易中的行为特征

• 批准号: ES/L01498X/1
• 负责人: Brian Francis
• 金额: $ 17.83万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FL01498X%2F1
• 关键词: Behavioural; characteristics; cyber; criminals; online

Online trading companies are increasingly being subjected to fraudulent attacks on their website. Such cyber attacks use invented customer information and stolen credit cards to place advertisements to attempt to sell non-existent goods to regular users. Other fraudulent activity includes phishing (attempting to gain access to login and password information through emails purporting to come from the company) and clone sites, which are constructed around similar-looking URLs. By working with such a company, this research project will investigate two aspects of this activity. Firstly it will measure the amount of such cybercrime activity, the nature of this activity, whether it is increasing and whether patterns of activity can be determined. It will compare the ilevel of such behaviour i na single company with the results of the Commercial Victimisation Survey of the Home Office. Secondly, it will focus on those fraudulent criminals who register on the website, and seek to determine how their pattern of behaviour and information added by the cyber-offender differs from that of a regular user. The research will harvest weblogs, examining the webpage history and activities of both regular users and cyber -criminals and combining that with intelligent use of information collected during the registration process. Bayesian Network analysis and other behavioural mining techniques wil be used to build a prototype system with the intention of providing early detection of fraudulent behaviour. One part of this will seek to use sociodemographic information from user postcodes to identify dissonance between the value of goods being advertised and the classification of the postcode.

在线贸易公司的网站越来越多地遭受欺诈性攻击。此类网络攻击利用发明的客户信息和被盗的信用卡来投放广告，试图向普通用户出售不存在的商品。其他欺诈活动包括网络钓鱼（试图通过声称来自公司的电子邮件获取登录名和密码信息）和克隆网站（这些网站围绕外观相似的 URL 构建）。通过与这样的公司合作，该研究项目将调查该活动的两个方面。首先，它将衡量此类网络犯罪活动的数量、活动的性质、是否在增加以及是否可以确定活动模式。它将把单个公司的此类行为的水平与内政部商业受害调查的结果进行比较。其次，它将重点关注那些在网站上注册的欺诈犯罪分子，并试图确定他们的行为模式和网络犯罪分子添加的信息与普通用户有何不同。该研究将收集网络博客，检查普通用户和网络犯罪分子的网页历史和活动，并将其与注册过程中收集的信息的智能使用相结合。贝叶斯网络分析和其他行为挖掘技术将用于构建原型系统，旨在提供欺诈行为的早期检测。其中一部分将寻求使用用户邮政编码中的社会人口统计信息来识别广告商品的价值与邮政编码分类之间的不一致。