Peng Wang (Indiana University Bloomington), Zilong Lin (Indiana University Bloomington), Xiaojing Liao (Indiana University Bloomington), XiaoFeng Wang (Indiana University Bloomington)

A new type of underground illicit drug promotion, illicit drug business listings on local search services (e.g., local knowledge panel, map search, voice search), is increasingly being utilized by miscreants to advertise and sell controlled substances on the Internet. Miscreants exploit the problematic upstream local data brokers featuring loose control on data quality to post listings that promote illicit drug business. Such a promotion, in turn, pollutes the major downstream search providers’ knowledge bases and further reaches a large audience through web, map, and voice searches. To the best of our knowledge, little has been done so far to understand this new illicit promotion in terms of its scope, impact, and techniques, not to mention any effort to identify such illicit drug business listings on a large scale. In this paper, we report the first measurement study of the illicit drug business listings on local search services. Our findings have brought to light the vulnerable and less regulated local business listing ecosystem and the pervasiveness of such illicit activities, as well as the impact on local search audience.

View More Papers

SynthCT: Towards Portable Constant-Time Code

Sushant Dinesh (University of Illinois at Urbana Champaign), Grant Garrett-Grossman (University of Illinois at Urbana Champaign), Christopher W. Fletcher (University of Illinois at Urbana Champaign)

Read More

An In-Depth Analysis on Adoption of Attack Mitigations in...

Ruotong Yu (Stevens Institute of Technology, University of Utah), Yuchen Zhang, Shan Huang (Stevens Institute of Technology)

Read More

Context-Sensitive and Directional Concurrency Fuzzing for Data-Race Detection

Zu-Ming Jiang (Tsinghua University), Jia-Ju Bai (Tsinghua University), Kangjie Lu (University of Minnesota), Shi-Min Hu (Tsinghua University)

Read More

Get a Model! Model Hijacking Attack Against Machine Learning...

Ahmed Salem (CISPA Helmholtz Center for Information Security), Michael Backes (CISPA Helmholtz Center for Information Security), Yang Zhang (CISPA Helmholtz Center for Information Security)

Read More