The popularity of online gambling could bring negative social impact, and many countries ban or restrict online gambling. Taking China for example, online gambling violates Chinese laws and hence is illegal. However, illegal online gambling websites are still thriving despite strict restrictions, since they are able to make tremendous illicit profits by trapping and cheating online players. In this paper, we conduct the first deep analysis on illegal online gambling targeting Chinese to unveil its profit chain. After successfully identifying more than 967,954 suspicious illegal gambling websites, we inspect these illegal gambling websites from five aspects, including webpage structure similarity, SEO (Search Engine Optimization) methods, the abuse of Internet infrastructure, third-party online payment, and gambling group. Then we conduct a measurement study on the profit chain of illegal online gambling, investigating the upstream and downstream of these illegal gambling websites. We mainly focus on promotion strategies, third-party online payment, the abuse of third-party live chat services, and network infrastructures. Our findings shed the light on the ecosystem of online gambling and help the security community thwart illegal online gambling.

Online underground economy is an important channel that connects the merchants of illegal products and their buyers, which is also constantly monitored by legal authorities. As one common way for evasion, the merchants and buyers together create a vocabulary of jargons (called "black keywords" in this paper) to disguise the transaction (e.g., "smack" is one street name for "heroin" [1]). Black keywords are often "unfriendly" to the outsiders, which are created by either distorting the original meaning of common words or tweaking other black keywords. Understanding black keywords is of great importance to track and disrupt the underground economy, but it is also prohibitively difficult: the investigators have to infiltrate the inner circle of criminals to learn their meanings, a task both risky and time-consuming. In this paper, we make the first attempt towards capturing and understanding the ever-changing black keywords. We investigated the underground business promoted through blackhat SEO (search engine optimization) and demonstrate that the black keywords targeted by the SEOers can be discovered through a fully automated approach. Our insights are two-fold: first, the pages indexed under black keywords are more likely to contain malicious or fraudulent content (e.g., SEO pages) and alarmed by off-the-shelf detectors, second, people tend to query multiple similar black keywords to find the merchandise. Therefore, we could infer whether a search keyword is "black" by inspecting the associated search results and then use the related search queries to extend our findings. To this end, we built a system called KDES (Keywords Detection and Expansion System), and applied it to the search results of Baidu, China's top search engine. So far, we have already identified 478,879 black keywords which were clustered under 1,522 core words based on text similarity. We further extracted the information like emails, mobile phone numbers and instant messenger IDs from the pages and domains relevant to the underground business. Such information helps us gain better understanding about the underground economy of China in particular. In addition, our work could help search engine vendors purify the search results and disrupt the channel of the underground market. Our co-authors from Baidu compared our results with their blacklist, found many of them (e.g., long-tail and obfuscated keywords) were not in it, and then added them to Baidu's internal blacklist.

High-generation FPD (Flat Panel Display) glass substrates need to be carried by large clean transfer robot. Due to the space of the deposited glass box is limited, the distance between the two substrates is very small, this requires the sag amount of the robot fork must be controlled within a certain range. The traditional design is completed only in robot manufacturing stage by adding a shim to compensate for this distortion, but this method can only make the fork be compensated at a determined location, deformation at other locations may not be considered. A new solution to the dynamic compensation for the robot fork is proposed. By establishing the robot FEA model and mechanism accuracy model respectively, the synthetic dynamic error is then obtained. One can just change the mechanism accuracy through rational allocation of design tolerances of the robot links to minimize the dynamic error.