Behavioural characteristics of cyber-criminals in online trading

在线贸易公司的网站正越来越多地受到欺诈性攻击。这类网络攻击利用虚构的客户信息和被盗的信用卡投放广告，试图将不存在的商品出售给经常使用的用户。其他欺诈性活动包括网络钓鱼(试图通过声称来自该公司的电子邮件获取登录和密码信息)和克隆网站，这些网站围绕着看起来相似的URL构建。通过与这样一家公司合作，本研究项目将调查这一活动的两个方面。首先，它将衡量此类网络犯罪活动的数量、这种活动的性质、是否在增加以及是否可以确定活动模式。它将把一家公司的此类行为水平与内政部商业受害者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性罪犯，并试图确定他们的行为模式和网络犯罪者添加的信息与常规用户的不同之处。这项研究将收获网络博客，检查常规用户和网络罪犯的网页历史和活动，并将其与注册过程中收集的信息的智能使用相结合。将使用贝叶斯网络分析和其他行为挖掘技术建立一个原型系统，以便及早发现欺诈行为。其中一部分将寻求使用来自用户邮政编码的社会人口信息，以确定广告中商品的价值与邮政编码分类之间的不协调。

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.