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Zhifeng Geng

dblp:201/9253 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 3

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Web and social media mining · 50% Information retrieval · 50%
Computer networks
1 paper
Network measurement and analytics · 100%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › query understanding
query analysis
0.312017
How to Learn Klingon without a Dictionary: Detection and Measurement of Black Keywords Used by the Underground Economy · IEEE Symposium on Security and Privacy 2017

Methods — techniques the papers use, named apart from their topics

text similarity clustering · 0.6
YearPublicationVenuePosition
2019 Casino royale: a deep exploration of illegal online gambling
abstract
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.
Kun Du, Yubao Zhang, Shuang Hao 0001, Zhou Li 0001, Mingxuan Liu 0006, Haining Wang 0001, Hai-Xin Duan, Yazhou Shi, XiaoDong Su, Zhifeng Geng
ACSAC12
2019 TL;DR Hazard: A Comprehensive Study of Levelsquatting Scams
Kun Du, Zhou Li 0001, Hai-Xin Duan, Shuang Hao 0001, Baojun Liu 0002, Yuxiao Ye, Mingxuan Liu 0006, XiaoDong Su, Zhifeng Geng, Zaifeng Zhang, Jinjin Liang
SecureComm (2)11
2017 How to Learn Klingon without a Dictionary: Detection and Measurement of Black Keywords Used by the Underground Economy
abstract
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.
Xiulin Ma, Kun Du, Zhou Li 0001, Hai-Xin Duan, XiaoDong Su, Zhifeng Geng
IEEE Symposium on Security and Privacy8