5 research outputs found
Adversarial Attack and Defense on Graph Data: A Survey
Deep neural networks (DNNs) have been widely applied to various applications
including image classification, text generation, audio recognition, and graph
data analysis. However, recent studies have shown that DNNs are vulnerable to
adversarial attacks. Though there are several works studying adversarial attack
and defense strategies on domains such as images and natural language
processing, it is still difficult to directly transfer the learned knowledge to
graph structure data due to its representation challenges. Given the importance
of graph analysis, an increasing number of works start to analyze the
robustness of machine learning models on graph data. Nevertheless, current
studies considering adversarial behaviors on graph data usually focus on
specific types of attacks with certain assumptions. In addition, each work
proposes its own mathematical formulation which makes the comparison among
different methods difficult. Therefore, in this paper, we aim to survey
existing adversarial learning strategies on graph data and first provide a
unified formulation for adversarial learning on graph data which covers most
adversarial learning studies on graph. Moreover, we also compare different
attacks and defenses on graph data and discuss their corresponding
contributions and limitations. In this work, we systemically organize the
considered works based on the features of each topic. This survey not only
serves as a reference for the research community, but also brings a clear image
researchers outside this research domain. Besides, we also create an online
resource and keep updating the relevant papers during the last two years. More
details of the comparisons of various studies based on this survey are
open-sourced at
https://github.com/YingtongDou/graph-adversarial-learning-literature.Comment: In submission to Journal. For more open-source and up-to-date
information, please check our Github repository:
https://github.com/YingtongDou/graph-adversarial-learning-literatur
Uncovering Download Fraud Activities in Mobile App Markets
Download fraud is a prevalent threat in mobile App markets, where fraudsters
manipulate the number of downloads of Apps via various cheating approaches.
Purchased fake downloads can mislead recommendation and search algorithms and
further lead to bad user experience in App markets. In this paper, we
investigate download fraud problem based on a company's App Market, which is
one of the most popular Android App markets. We release a honeypot App on the
App Market and purchase fake downloads from fraudster agents to track fraud
activities in the wild. Based on our interaction with the fraudsters, we
categorize download fraud activities into three types according to their
intentions: boosting front end downloads, optimizing App search ranking, and
enhancing user acquisition&retention rate. For the download fraud aimed at
optimizing App search ranking, we select, evaluate, and validate several
features in identifying fake downloads based on billions of download data. To
get a comprehensive understanding of download fraud, we further gather stances
of App marketers, fraudster agencies, and market operators on download fraud.
The followed analysis and suggestions shed light on the ways to mitigate
download fraud in App markets and other social platforms. To the best of our
knowledge, this is the first work that investigates the download fraud problem
in mobile App markets.Comment: Published as a conference paper in IEEE/ACM ASONAM 201